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    "# Chapter 4\n",
    "`Original content created by Cam Davidson-Pilon`\n",
    "\n",
    "`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) at Quantopian (@quantopian)`\n",
    "\n",
    "`Ported to PyMC last by Kurisu Chan (@miemiekurisu) `\n",
    "______\n",
    "\n",
    "## The greatest theorem never told\n",
    "\n",
    "\n",
    "This chapter focuses on an idea that is always bouncing around our minds, but is rarely made explicit outside books devoted to statistics. In fact, we've been using this simple idea in every example thus far. "
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    "### The Law of Large Numbers\n",
    "\n",
    "Let $Z_i$ be $N$ independent samples from some probability distribution. According to *the Law of Large numbers*,  so long as the expected value $E[Z]$ is finite, the following holds,\n",
    "\n",
    "$$\\frac{1}{N} \\sum_{i=1}^N Z_i \\rightarrow E[ Z ],  \\;\\;\\; N \\rightarrow \\infty.$$\n",
    "\n",
    "In words:\n",
    "\n",
    ">   The average of a sequence of random variables from the same distribution converges to the expected value of that distribution.\n",
    "\n",
    "This may seem like a boring result, but it will be the most useful tool you use."
   ]
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    "### Intuition \n",
    "\n",
    "If the above Law is somewhat surprising,  it can be made more clear by examining a simple example. \n",
    "\n",
    "Consider a random variable $Z$ that can take only two values, $c_1$ and $c_2$. Suppose we have a large number of samples of $Z$, denoting a specific sample $Z_i$. The Law says that we can approximate the expected value of $Z$ by averaging over all samples. Consider the average:\n",
    "\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\;Z_i $$\n",
    "\n",
    "\n",
    "By construction, $Z_i$ can only take on $c_1$ or $c_2$, hence we can partition the sum over these two values:\n",
    "\n",
    "\\begin{align}\n",
    "\\frac{1}{N} \\sum_{i=1}^N \\;Z_i\n",
    "& =\\frac{1}{N} \\big(  \\sum_{ Z_i = c_1}c_1 + \\sum_{Z_i=c_2}c_2 \\big) \\\\\\\\[5pt]\n",
    "& = c_1 \\sum_{ Z_i = c_1}\\frac{1}{N} + c_2 \\sum_{ Z_i = c_2}\\frac{1}{N} \\\\\\\\[5pt]\n",
    "& = c_1 \\times \\text{ (approximate frequency of $c_1$) } \\\\\\\\ \n",
    "& \\;\\;\\;\\;\\;\\;\\;\\;\\; + c_2 \\times \\text{ (approximate frequency of $c_2$) } \\\\\\\\[5pt]\n",
    "& \\approx c_1 \\times P(Z = c_1) + c_2 \\times P(Z = c_2 ) \\\\\\\\[5pt]\n",
    "& = E[Z]\n",
    "\\end{align}\n",
    "\n",
    "\n",
    "Equality holds in the limit, but we can get closer and closer by using more and more samples in the average. This Law holds for almost *any distribution*, minus some important cases we will encounter later.\n",
    "\n",
    "##### Example\n",
    "____\n",
    "\n",
    "\n",
    "Below is a diagram of the Law of Large numbers in action for three different sequences of Poisson random variables. \n",
    "\n",
    " We sample `sample_size = 100000` Poisson random variables with parameter $\\lambda = 4.5$. (Recall the expected value of a Poisson random variable is equal to its parameter.) We calculate the average for the first $n$ samples, for $n=1$ to `sample_size`. "
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\n",
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from IPython.core.pylabtools import figsize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mtl\n",
    "mtl.style.use(\"ggplot\")\n",
    "\n",
    "\n",
    "figsize( 12.5, 5 )\n",
    "\n",
    "sample_size = 100000\n",
    "expected_value = lambda_ = 4.5\n",
    "poi = np.random.poisson\n",
    "N_samples = range(1,sample_size,100)\n",
    "\n",
    "for k in range(3):\n",
    "\n",
    "    samples = poi( lambda_, sample_size ) \n",
    "    \n",
    "    partial_average = [ samples[:i].mean() for i in N_samples ]\n",
    "    \n",
    "    plt.plot( N_samples, partial_average, lw=1.5,label=\"average \\\n",
    "of  $n$ samples; seq. %d\"%k)\n",
    "    \n",
    "\n",
    "plt.plot( N_samples, expected_value*np.ones_like( partial_average), \n",
    "    ls = \"--\", label = \"true expected value\", c = \"k\" )\n",
    "\n",
    "plt.ylim( 4.35, 4.65) \n",
    "plt.title( \"Convergence of the average of \\n random variables to its \\\n",
    "expected value\" )\n",
    "plt.ylabel( \"average of $n$ samples\" )\n",
    "plt.xlabel( \"# of samples, $n$\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how *jagged and jumpy* the average is initially, then *smooths* out). All three paths *approach* the value 4.5, but just flirt with it as $N$ gets large. Mathematicians and statistician have another name for *flirting*: convergence. \n",
    "\n",
    "Another very relevant question we can ask is *how quickly am I converging to the expected value?* Let's plot something new. For a specific $N$, let's do the above trials thousands of times and compute how far away we are from the true expected value, on average. But wait &mdash; *compute on average*? This is simply the law of large numbers again! For example, we are interested in, for a specific $N$, the quantity:\n",
    "\n",
    "$$D(N) = \\sqrt{ \\;E\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\;\\;\\right] \\;\\;}$$\n",
    "\n",
    "The above formulae is interpretable as a distance away from the true value (on average), for some $N$. (We take the square root so the dimensions of the above quantity and our random variables are the same). As the above is an expected value, it can be approximated using the law of large numbers: instead of averaging $Z_i$, we calculate the following multiple times and average them:\n",
    "\n",
    "$$ Y_k = \\left( \\;\\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\; \\right)^2 $$\n",
    "\n",
    "By computing the above many, $N_y$, times (remember, it is random), and averaging them:\n",
    "\n",
    "$$ \\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k \\rightarrow E[ Y_k ] = E\\;\\left[\\;\\; \\left( \\frac{1}{N}\\sum_{i=1}^NZ_i  - 4.5 \\;\\right)^2 \\right]$$\n",
    "\n",
    "Finally, taking the square root:\n",
    "\n",
    "$$ \\sqrt{\\frac{1}{N_Y} \\sum_{k=1}^{N_Y} Y_k} \\approx D(N) $$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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331VY5+XlVeV29957L0eOHGHatGk0aNAAT09Pbr311jLncOutt/LYY4+xePFievfuzZIlS8pcXHI4HNx11108/fTTFfZf+hmyyhJqd44Plf/9lD4+wPz584mOjq6wvqRBUpkz7fdM60svr6zulf5dBQYGMmTIED7//HNGjhzJ559/zuDBg13fzbnUz8q+w7OdQ1Uqq/ul66C7+92/fz+1atWSJF+Is3Ar0d+5c2eZz0VFRfz555+Eh4dXS1BCiAvXokULUlNT2b17t+uqflFREevXr2fcuHGucn369GHevHmYTCbXQ7d9+vThu+++Y9OmTbz66qsXFEdaWhq7d+9myZIlrkbD0aNHK1xJ1uv1xMbGEhsby/PPP0/z5s358ssv6dChgys5sNvtFzxiypmOUxmTyXRekwPGxMSQmZlJYWEhLVu2PKdt69Spw+jRoxk9ejSDBg3itttu4/333yc+Pp6TJ08ydepUmjVrBsCaNWvOmIyfq7/++stVX2w2Gxs2bKhyKOWYmBg+//xz/Pz8qFWrltvHWLVqFdOmTWPo0KEA5OXlceDAgTLfU1BQEDfccAOff/45ycnJ+Pv7M3DgwDLH3r59Ow0bNjznpPNsxy85/7Vr19K3b1/A+V1s2rTJldS3aNECDw8PDhw4wKBBg9w+docOHfj444/Jy8urNIFu0aIFv//+e5llv//+O5qmlbk75467776bESNGsHfvXn788UfmzZvnWnch9bN58+asXbu2zL8jf/311znto6r9zpo1i6ysLNdV/b1795KZmVmh7OjRoxkyZMgFH1OIq51bif4HH3xQ5rOHhweRkZE8+uij1RKUEOLC9enTh06dOnH77bczY8YM/P39eeGFFygsLGTs2LFlyk2ePBm9Xk/v3r1dy26++WaMRqPbt+SrEhgYSEhICB9//DENGzYkLS2NiRMnluna8f3333PgwAFiY2MJCQlh06ZNJCYmuhKbBg0aALBo0SJ69OiBp6dnhS4b7jjbcSrToEEDVq5cyf79+/H39y/TreBM+vTpQ1xcHCNGjODVV1+lTZs2ZGRksGbNGjw8PHjggQcq3e6RRx5h0KBBNGnShMLCQhYsWEBERAS+vr5ERkZiNpt59913+de//sWhQ4d4+umnz/vqamVeeeUVQkNDadCgAW+88QbJycll6ktpd9xxB2+++SaDBw9m6tSpREdHk5yczIoVK2jWrBnDhw+vdLsmTZrwv//9jx49emC323nuuecqbUzdc8893Hzzzezfv5/bbrutzB2JZ555hk6dOnHnnXfy6KOPEhISwqFDh1i4cCGPPvooUVFRVZ7j2Y7fuHFjhgwZwsMPP8yHH35ISEgI06dPJzs72/Vd+/j48Mwzz7hGoenXrx82m40dO3awZcuWKhvI48aN48MPP2TYsGE8//zzhIWFsWvXLvR6PQMHDuTJJ5+kffv2PP744zz44IMcOnSIf/7zn9xxxx3nPEnlwIEDCQoK4tZbb8XX17dMg+R86yfAv/71L0aNGkWnTp0YOHAga9as4fPPPwfO/0o/OOvTc889x913380LL7xAQUEB//rXv/D09Kyw32+//ZbvvvuOX3/99byPJ8Q1oSYfEBBCnDt3R91RSqnjx4+rUaNGKX9/f+Xh4aFiY2PVhg0bymxjs9mUn5+fat26tWtZRkaG0uv1VR7nTCp7GPe3335TrVu3VmazWUVHR6tvvvlGNWzY0PXQ6e+//6569+6tgoODldlsVo0aNVIvv/yycjgcrn08+uijqlatWkrTtLOOtFNa6e/EneOUt3//fnXddde5HnZeuXJlhYdSS5Q+J6Wco9I89dRTqn79+spoNKratWurAQMGqF9//bXK440bN041btxYeXh4qKCgIDVo0CC1c+dO1/r58+erRo0aKbPZrNq2bat+++03pdfrXYMjVBZbYmKiK/YSJQ9CLlu2TCl1+mHJ77//XrVv316ZTCbVrFkz14OSVe07NTVVPfTQQyosLEwZjUYVFhamhg8frjZv3lzlOW7fvl117dpVeXh4qMjISDVjxoxKH3q2Wq0qJCREAWrjxo2V7mfo0KEqICBAeXh4qIYNG6oHHnjA9cBuVQ+Gu3P81NRUddNNNylPT08VEhKinn32WXXzzTerG264ocy+Zs2apdq0aaPMZrMKCAhQnTp1co0MVJWSkZD8/PyUp6enat26tWvUHaWU+vHHH12/g+DgYPXQQw+VeXi3sn8DqnpgfcKECQpQjzzySIV1Z6ufVdVzpZR64403VFhYmPLw8FD9+/dXH374oQJcI2lV9TBu+RGuStddpZwPiXfu3FmZTCbVqFEjNX/+fBUSElLmQXKlnL/byMjIyr5eIUQpmlKV3/M9W7/NEjqd20PxCyGEuEz99ttv9O7dm8TEROmWWQm73U7Tpk0ZOnQo06dPr+lwLjv//e9/efvtt0lLS7uo+z18+DD169dn0aJF0lVHiPNQZded2267za0dlO7zJ4QQQlwNVq1aRUpKCu3atSMnJ4c333yTQ4cOndNkbVer4uJipk+f7ppEa+XKlbz22ms8/PDDF7zvL774grp169KgQQMOHz7MxIkTiYyMpH///hchciGuPVUm+u+9996ljEMIIYS4bNjtdl588UUSEhIwGo20bNmSlStX0qpVq5oOrcaVzBA9ffp0cnJyaNCgAc888wxPPvnkBe87LS2NKVOmcOzYMYKCgujevTvz588vM5SoEMJ9VXbdEUIIIYQQQly53B5Hf+PGjezevZvs7Owyyx955JGLHpQQQgghhBDiwrj1JO38+fP56KOPcDgc/PXXX/j4+LBt27YzTogihBBCCCGEqDluXdFfuXIlkydPpl69evz222/ce++99OjRg2+//ba647sgx48fv+B9BAcHk5qaehGiEVcLqROiMlIvRHlSJ0RlpF6I8s61ToSFhbld1q0r+nl5ea6JOgwGAzabjUaNGrF79263DySEEEIIIYS4dNy6oh8aGkpiYiIRERFERESwdOlSfHx8zmlmyq1btzJ79mwcDgd9+/atMGOiUorZs2ezZcsWzGYz48aNIyoqiuPHj/Pmm2+6yqWkpHDLLbcwePBgt48thBBCCCHEtcatRH/UqFHk5OQAzimq3377bQoLC7n//vvdOojD4eCTTz5h8uTJWCwWJk2aRExMTJlJWbZs2UJSUhLvvPMO8fHxzJo1i5deeomwsDBee+01137+8Y9/0KlTp3M9TyGEEEIIIa4pbiX67du3d71v1KgR77777jkdJCEhgdDQUGrXrg1At27d2LBhQ5lEf+PGjcTGxqJpGtHR0eTl5ZGRkUFgYKCrzI4dOwgNDSUkJOScji+EEEKIK4dSisLCQhwOB5qm1XQ4F1VycjJFRUU1HYa4jFRWJ5RS6HQ6PDw8LuhvwK1Ef9q0aVx33XV06NABk8l0zgdJT0/HYrG4PlssFuLj4yuUCQ4OLlMmPT29TKL/559/0r179yqPs3z5cpYvXw7AK6+8UmZ/58tgMFyU/Yirh9QJURmpF6I8qRPnLy0tDQ8PD4xGY02HUi1kAjBRXmV1ori4GJ1OVyaHPlduJfrNmzdn0aJFzJw5k44dO9KjRw9at26NTufWs7xUNidX+dbJ2crYbDY2bdrE7bffXuVx4uLiiIuLc32+GE+1y9PxojypE6IyUi9EeVInzl9eXh7e3t7YbLaaDuWiKxnURIgSVdUJTdPIzc2tkCOfy6g7biX6N9xwAzfccAMnTpzgjz/+YM6cOeTm5tK1a1fGjBlz1u0tFgtpaWmuz2lpaWWu1JeUKf0PYvkyW7ZsoUGDBgQEBLgTco1Rmemo7RvQ6kaiNWxa0+EIIYQQV5yrrbuOEOfrQv8W3Lskf0qdOnUYOXIkjz76KJGRkfzyyy9ubdewYUNOnDhBSkoKNpuNNWvWEBMTU6ZMTEwMq1atQinFvn378PLyOqduO5cDx8olOJ68FzV3Bmq1e9+NEEIIIYQQ1cHtRD8pKYlvv/2Wxx9/nKlTpxIaGsrzzz/v1rZ6vZ4xY8YwdepUHnvsMbp27eoapnPp0qUAtGvXjlq1ajF+/Hg+/PDDMiP6FBUVsX37djp37nyOp3dpafUbud6r7RtRDnsNRiOEEEKIK8nOnTv59ddfz3m7m2++mW3btp2xzJo1a7j77rsBWLp0Ke+9995Fj+NiKB2nuz7++GMKCgqqKaIrm1tddyZNmsTx48fp2LEjd911F23atHG7f36J9u3blxm9B6B///6u95qmVTlcp9ls5tNPPz2n49WIyEbgHwhZGZCTBQf2QaNmNR2VEEIIIa4Au3btYvv27fTt27daj9O/f/8yOVhNxXGxzJo1i5tuuglPT8+aDuWy41a2PmTIED7++GMeeeQR2rVrd85J/rVC0+nQWnd0fVbb1tdgNEIIIYQ4X99++y2DBw+mX79+TJw4EbvdztatW4mLi6OwsJD8/Hx69+7Nnj17WLNmDSNGjOC+++6jV69ePPXUUzgcDgB+//13hgwZwoABA3jwwQfJy8sDnBOJDh06lLi4OAYPHkx2djavv/46ixYtol+/fnz//ffk5+fz+OOPM2jQIPr37+/qMl1QUMDYsWOJi4vjoYceorCwsNJzWLlyJbGxsQwfPpyffvrJtXzevHn8+9//BmDx4sX06dOHuLg4RowYgdVqrRDHli1bGDp0KP3792fo0KEkJCS49nP//fdzxx130L17d1588cUyxx4wYABxcXHccsstAFWeT3m5ubluf5effPIJycnJjBw5kptvvplFixbxn//8B3A2ALp27QrAoUOHXJO1bt++nZtuuonrr7+e22+/neTkZFeZO+64g+uvv54bb7zRdZ4TJkzg2WefZejQoXTt2pUffvjB3WpU49y6ot+tW7fqjuOqobXphFrt7I6ktq2Hm+6p4YiEEEIIcS7i4+NZtGgRCxcuxGg0MmnSJBYsWMDIkSPp168f06ZNo7CwkBEjRtC0aVPWrFnD1q1bWblyJeHh4dxxxx0sWbKEbt268fbbbzNv3jy8vLyYMWMGM2fOZOzYsYwdO5YPPviAtm3bkpOTg6enJ0888QTbt29n6tSpALz88st0796dN954g6ysLAYPHsx1113H3Llz8fT0ZPny5ezevZvrr7++wjkUFhby5JNP8vXXX9OgQQMeeuihSs/1rbfe4n//+x916tQhKysLk8lUIY6cnBwWLFiAwWBg1apVvPrqq3z88ceA8+r/L7/8gslkIjY2ltGjR+Ph4cGTTz7JggULqFevHhkZGQC8/fbblZ6Pl5dXmZjc/S4/+ugjHnvsMT766CPmz59PUFAQKSkpzJw5E4B169YRGBjIiRMnWL9+PZ07d6a4uJjJkycze/ZsLBYL33//Pa+++ipvvPEGEydO5JVXXiEqKorNmzczadIk5s+fDzjHul+4cCEJCQmMHj2aG2644SLUtOrnVqIvzkHTNmAygdUKJxJRKcfRark/DJIQQgghatYff/zBjh07GDRoEOBMmkvmRHjssccYNGgQHh4evPDCC65t2rZtS2RkJADDhw9n/fr1mM1m9u3bx7BhwwDnuOgxMTHs37+fWrVq0bZtWwB8fX0rjWPVqlUsW7bMlbgWFRVx7Ngx1q1b5xr1sHnz5jRrVrGbcEJCAvXq1SMqKgqAm266iS+++KJCuZiYGB577DGGDBnCwIEDK40jOzubCRMmcPDgQTRNo7i42LWuR48e+Pn5ARAdHc2xY8fIzMykS5cu1KtXD8A1uEpV59O4ceMyx3P3u+zQoUOFWGvVqkVeXh65ubmcOHGC4cOHs27dOtavX8/AgQPZv38/e/fu5dZbbwXA4XC4ttm0aRP/+Mc/XPuyWq2u99dffz06nY7o6GhOnjxZ6fd0OZJE/yLTzGbSm3dBt2sTAcV5qG0b0PoNq+mwhBBCCOEmpRQjR45k0qRJFdZlZmaSn5+PzWajqKjIdTW6/DCImqahlCI2Npb333/ftdxgMLB9+3a3hk1USvHRRx/RqFGjCuvc2d6dMq+++iqbN2/m119/pX///q5BUkp77bXX6NatG5988gmJiYncfPPNrnWlJ1LV6XSu8eArO/aZzudMcVf1XValQ4cOzJs3j6ioKDp37sxXX33Fpk2beO655zh27BjR0dEsXry4zDY5OTn4+fmxbNmySvdZ+jwrm/vpciWd7S+y+TtTuT/gBpaGOfuEST99IYQQ4srSo0cPfvjhB9f8PhkZGRw9ehSAiRMn8uSTT3LjjTe6uraAs7vJkSNHcDgcLFq0iE6dOtGhQwc2bNjAwYMHAWff+v3799OoUSOSk5PZunUr4OyTbrPZ8PHxITc317XPnj17Mnv2bFdiuXPnTgA6d+7Md999B8CePXv4+++/K5xDo0aNOHLkCIcOHQJg4cKFlZ7roUOHaN++PU8++SRBQUEcP368Qhw5OTmEhoYC8PXXX5/1++vQoQNr167lyJEjru/vTOdT3rl8l0CFeDt37szMmTPp0qULLVu2ZM2aNZhMJvz8/GjYsCHp6els3LgRcN4Z2Lt3L76+vkRERLgaAEopdu3addZzvdy5negfPXqUb775hlmzZgFw7NgxDh8+XG2BXamiAj0A+CukpXNB/C5UXu4ZthBCCCHE5SQ6OpqJEydy2223ERcXx2233UZycjLz58/HYDBw44038sgjj7Bt2zb++OMPwDm64EsvvUSfPn2IiIhg4MCBWCwW3nzzTR5++GHi4uIYMmQI8fHxmEwmPvjgAyZPnkxcXBy33norRUVFdOvWjfj4eNdDsBMmTKC4uJi4uDj69OnDtGnTALj77rvJy8sjLi6O999/39UFqDQPDw+mTZvG3XffzfDhwwkPD6/0XF988UX69u1Lnz596NKlCy1atKgQx9ixY3n55ZcZNmwYdvvZhw63WCxMmzaN+++/n7i4OMaOHQtQ5fmU5+53WZLo33HHHdx5552uOw2dO3fm+PHjdO7cGb1eT1hYGJ06dQKcV+Y//PBDXnrpJeLi4ujfv78r6X/vvff46quviIuLo3fv3pXe3bjSaMqN+w9r167lk08+oVOnTvz555/MmTOH/fv38+WXX/Lss89eijjPy/Hjxy94H+c6hXmx3cHd3yaQX+zg/b9eIbQwHe3+f6Hr3POCYxGXB5nWXlRG6oUoT+rE+cvPz6/wgOblbM2aNcycOZPPP//8rGUNBoOre4sQcOY6UdnfQliY+89+unVF/+uvv2by5Mk8+OCDrqE1IyMjXbeDxGlGvY6Yuj5Aqav60n1HCCGEEEJcYm4l+llZWa6nn0tomubWQx7Xoq4RzkR/XbAz0Vc7N6FsxWfaRAghhBBXqG7durl1NV+IS82tRD8qKopVq1aVWfbnn3+e9anpa1X7MB9Meo29/vVJN/lBQT7E767psIQQQgghxDXErUR/9OjRfPXVV0yZMoWioiKmTp3KvHnzuOcemQyqMh4GHe3qeGPAQYKv8+EXGX1HCCGEEGeyc+dOfv3113Pe7uabb2bbtm0XfPyLtZ8LtWbNGu6+++6aDuOq4NY4+nXr1uWtt95i06ZNdOjQAYvFQocOHfDw8Kju+K5Y93WoxT+D0vD6zXklX21dhxp1v3R3EkIIIUSldu3axfbt2+nbt29NhyKuEm4Pr2k2m+nWrRtDhw6le/fukuSfRW0fEz7NmoPnqSel01LgmAxHKoQQQlwJvv32WwYPHky/fv2YOHEidrudrVu3EhcXR2FhIfn5+fTu3Zs9e/awZs0aRowYwX333UevXr146qmncDgcAPz+++8MGTKEAQMG8OCDD5KXlwc4x4ofOnQocXFxDB48mOzsbF5//XUWLVrkGtYyPz+fxx9/nEGDBtG/f39++eUXwDmG/NixY4mLi+Ohhx6isLCwQvwrVqwoM8vrmjVrXD0xnn76aQYOHEjv3r15/fXXKz3/0rPV/vDDD0yYMAGAtLQ0HnjgAQYNGsSgQYPYsGFDhW0TExO58cYbGTBgAAMGDHCVWbNmDTfffDMPPPAAsbGxPPLII64x9VeuXElsbCzDhw/np59+OqfflaiaW1f0n3vuuSqvRD///PMXNaCriWYw4mjZgZytm/EvzkNtW48WXr+mwxJCCCHEGcTHx7No0SIWLlyI0Whk0qRJLFiwgJEjR9KvXz+mTZtGYWEhI0aMoGnTpqxZs4atW7eycuVKwsPDueOOO1iyZAndunXj7bffZt68eXh5eTFjxgxmzpzJ2LFjGTt2LB988AFt27YlJycHT09PnnjiCbZv3+6aiOvll1+me/fuvPHGG2RlZTF48GCuu+465s6di6enJ8uXL2f37t1cf/31Fc4hNjaWp556yjU846JFixg6dCgATz31FIGBgdjtdkaNGsXu3btp3ry5W9/Nc889xwMPPECnTp04duwYt99+O7///nuZMsHBwfzf//0fHh4eHDhwgIcfftiVvO/cuZMVK1YQGhrKsGHD2LBhA61bt+bJJ5/k66+/pkGDBjz00EMX8usTpbiV6Pfp06fM58zMTFauXMl1111XLUFdLXYm5/Oq32CaNIli0s45zn76g2+p6bCEEEIIcQZ//PEHO3bsYNCgQQAUFhYSHBwMwGOPPcagQYPw8PDghRdecG3Ttm1b1wiFw4cPZ/369ZjNZvbt28ewYcMA5yysMTEx7N+/n1q1arkmuvL19a00jlWrVrFs2TJmzpwJQFFREceOHWPdunWMGTMGgObNm9OsWbMK2xoMBnr37s2yZcsYPHgwv/76K5MnTwZg8eLF/O9//8Nut5OcnEx8fLzbif7q1avZt2+f63Nubi65ubn4+Pi4lhUXF/Pvf/+b3bt3o9PpOHDgQJnvqWQc+BYtWpCYmIiXlxf16tUjKioKgJtuuokvvvjCrXjEmbmV6Pfq1avCsi5duvD++++7ZiETFYX5mcix69gaFE2B3oTnwX2orAw0/8CaDk0IIYQQVVBKMXLkSCZNmlRhXWZmJvn5+dhsNoqKilyTGZXv+aBpGkopYmNjef/9913LDQYD27dvd+uZPaUUH330UaWjHLqz/ZAhQ5gzZw4BAQG0bdsWHx8fjhw5wocffsiPP/5IQEAAEyZMqLTrT+n9FxUVud47HA4WLVqEp6dnlcf9+OOPCQkJYdmyZTgcDlcCD86ZaUvo9XrXRFHyDGP1cCvRr0xQUBCHD0uf8zMJ8jTQNMSTv0/ClqAmdDu5A7V9A9p1/Ws6NCGEEOKKYH9gaLXtW//xokqX9+jRg9GjR/PAAw8QHBxMRkYGeXl5hIeHM3HiRJ588kmOHDnC1KlTXd1stm7dypEjRwgPD2fRokXccccddOjQgX//+98cPHiQBg0aUFBQQEpKCo0aNSI5OZmtW7fStm1bcnNz8fDwwMfHh9zcXFccPXv2ZPbs2bz44otomsbOnTtp2bIlnTt35rvvvqN79+7s2bOHv//+u9Lz6NatG0888QT/+9//GDJkCICrm5Cfnx8nT55k5cqVdO3atcK2ISEhxMfH07BhQ37++We8vb1dMX322WeMHTsWwBVTadnZ2dSpUwedTsf8+fOx2+1n/D00atSII0eOcOjQIerXr8/ChQvPWF64z61Ef8WKFWU+W61W1q1bR3R0dLUEdTXpGuHL3ycL+Cu4lTPR37YeJNEXQgghLlvR0dFMnDiR2267DaUUBoOBqVOnsnbtWgwGAzfeeCN2u51hw4bxxx9/oNPpaN++PS+99BJ79uyhc+fODBw4EJ1Ox5tvvsnDDz+M1WoFnA/CRkZG8sEHHzB58mQKCwvx8PBg3rx5dOvWjRkzZtCvXz8eeeQRJkyYwJQpU4iLi0MpRXh4OJ9//jl33303jz/+OHFxcTRv3tzVBag8vV5PXFwcX3/9NW+//Tbg7C7TsmVLevfuTb169ejYsWOl206aNIl77rmHsLAwmjRp4nqI+IUXXuCZZ54hLi4Om81G586defXVV8tse8899/Dggw/yww8/0L17d9ddj6p4eHgwbdo07r77boKCgujUqRN79uw5l1+ZqIKmSh53PoPyD9yazWbq16/P4MGDq+xXdjk4fvz4Be8jODiY1NTU894+OdfKg98fwMNWxGdrnsdk0KF7439oZvMFxyZqxoXWCXF1knohypM6cf5KHiCFmrmif67WrFnDzJkz3Zod12AwuLqrCAFnrhOl/xZKlDzj4Na+z1bA4XBw00030aRJE4xGo9s7Fk61fUxEBZo5kAHbAxoRk74H9myDNp1qOjQhhBDisnexknEhrkVnHUdfp9Mxbdo0SfIvQJcI512P7YHOMWllllwhhBDi6tGtWze3ruYLcam51Ue/WbNm7Nu3T/rkn6e4hv60s58k6rfFAKjtG1AOB5rO7fnKhBBCCCGEOCduJfohISG8/PLLxMTEYLFYygyBNGrUqGoL7mph8TIS1Doah68/5GRBVgYcToAG0nASQgghhBDVw61LylarlY4dO6JpGunp6aSlpblewj2aTo/WOgarztm2Ulul+44QQgghhKg+bl3RHzdu3AUfaOvWrcyePRuHw0Hfvn0ZPnx4mfVKKWbPns2WLVswm82MGzfONcFCXl4eM2fOJDExEU3TGDt27BXXjSi70MYr/n1I7tSJmX+9gm7bOrjxzpoOSwghhBBXuU8++YTPP/+cVq1a8d5777mWz507l6effprffvuNxo2dzxH27NmTL774goiIiGqNafr06Xh7e/PQQw9V63HO1dChQ1m0qOoHwBMTE7nnnnsqDD0P0LhxY+Lj46szvHPm1hX90aNHV7r8/vvvd+sgDoeDTz75hGeeeYY333yTP//8k6NHj5Yps2XLFpKSknjnnXd48MEHmTVrlmvd7Nmzadu2LW+99RavvfYadevWdeu4lxMfs54k5UGqRyD7fcPh2GFUanJNhyWEEEKIS0QphcPhuOTHnTNnDnPnzi2T5AP8/ffftGjRgl9//RVwzoCbmppKeHh4lfuqqXOobiXndaYk/0rkVqJf2YxmNpvN7V90QkICoaGh1K5dG4PBQLdu3diwYUOZMhs3biQ2NhZN04iOjiYvL4+MjAzy8/P5+++/6dOnD+Aca7RkdrYriU7T6FLPD4C/QpwzyKltG860iRBCCCFq0JgxY7j++uvp3bs3X3zxBQBTp07ls88+c5WZPn06M2fOBODbb79l8ODB9OvXj4kTJ2K320lMTKRnz55MmjSJAQMGcOzYsUr3C/Dmm28SGxvLrbfeyrhx486438p8+OGH9OnThz59+vDxxx8D8NRTT3HkyBFGjx7NRx99VKb8nj17ePjhh1m+fDkA+/bto1GjRmWexQQqnMPx48crPYeSck8++SS9e/fmtttuo6CgwLWft99+m+uuu45Ro0axf//+M8admJhIbGwsTzzxBH369OGRRx5h1apVDBs2jO7du7Nly5YK53+m382Z4i19XiV3NqraBpw58KOPPkpcXBwPPPBAmXMsUdnvLD8/n7vuuou4uDj69OnD999/X+nv8WI6Y6L/3HPPMWXKFIqLi5kyZUqZ14QJE9zuPpOeno7FYnF9tlgspKenVygTHBxcoUxKSgp+fn68//77TJw4kZkzZ1JYWHgu53jZ6BrhA8Bfwa1QgNq2rmYDEkIIIUSVpk+fzs8//8ySJUv49NNPSU9PZ9iwYSxevNhVZvHixQwZMoT4+HgWLVrEwoULWbZsGXq9ngULFgCwf/9+br75ZpYuXUpERESl+922bRtLlizhl19+YdasWWzbtg3gjPstbfv27Xz99df88MMPLF68mC+//JKdO3fy6quvUrt2bebPn8+DDz5YZpt9+/bRv39/0tLSyM7O5u+//6Zp06aVfhelzyE8PLzScwA4ePAg99xzDytXrsTPz48lS5a44lu0aBFLly4tc35VxQ1w6NAh7rvvPpYvX05CQgILFy5k4cKFPPfcc7z77rsVYqzqd1PV77Ky8zrb779kmzvvvJPly5fj6+vLnDlzymxX1e9s5cqVhIaGsnz5clasWEHv3r0r/a4vpjP20S+5ip6QkFAmGE3T8Pf3p2XLlm4dpLLJd8u3FqsqY7fbOXjwIGPGjKFx48bMnj2bhQsXcuutt1Yov3z5cler9JVXXinTcDhfBoPhouwHIDbIQsDq45wgmCPetYnct5MgTw903j4XZf/i0riYdUJcPaReiPKkTpy/5ORkDAa3HiOsVp999pkrUT1+/DhHjhwhJiaGtLQ0UlNTSUtLIyAggMjISD755BN27NjB4MGDASgsLKRWrVro9XrCw8Pp3LnzGfe7adMmBg4ciK+vc+6dAQMGoNPpWLNmTaX7Lf/9bNy4kUGDBuHn5+w9MHjwYDZs2EDbtm3RNA29Xl9mm2PHjhEUFISvry89e/Zk9erV7N27l5YtW1bYt7vnUKtWLerVq0fbtm0BaNu2LceOHcNgMLBhwwYGDRpU4fyqinvAgAHUq1ePVq1aAdC0aVNiY2MxGo20aNGCo0ePVoizbdu2lf5uzhRv+fMCXPutapu6devStWtXAEaOHMmsWbN45JFHXNtW9TsbMWIEL7zwAi+//DL9+vWjS5cuFY5ZntlsvqB/R874V9SrVy/A+XDBhfSLt1gsZUboSUtLIzAwsEKZ0lOFl5TRNA2LxeK6ldKlSxcWLlxY6XHi4uKIi4tzfb4YU49f7CnMO4b7sGx/FuuCWxF5eDmpq5aj69jjou1fVD+Z1l5URuqFKE/qxPkrKipCr9fXaAxr1qzh999/Z9GiRXh6enLzzTeTn5+PzWZj0KBBfP/996SkpDB06FBsNht2u52RI0cyadKkMvtJTEzEy8sLm80GwLp16yrdr91ux+FwuMo5HA4cDkeV+y0pV6Kq7W02G0op7HZ7mW127NhBkyZNsNls9O7dmwULFpCSkkL//v0r3Xfpc6jqu7Hb7ZhMJlc5TdMoLi52dfdWSlV6flWdd+l9gTMZLjkfm81WIU6g0t/NmeItfV6lv9szbVP6+y//+Ux1AeCnn35ixYoVvPjii/Ts2ZPHHnvMdV6VKXluorSwsLBKy1bGrT76F/rwa8OGDTlx4gQpKSmuLy8mJqZMmZiYGFatWoVSin379uHl5UVgYCABAQFYLBaOHz8OOCvmmR4Sudx1PTVL7vrg5s4F0n1HCCGEuOzk5OTg7++Pp6cnCQkJbN682bVu2LBhfP/99/z444+uq7Y9evTghx9+cCVlGRkZFQYeAcjOzq50v506dWLZsmUUFhaSl5fnekDW3f126dKFX375hYKCAvLz8/n5558rXKkubc+ePTRr1sy17fr168/Ydcfd76YqXbp04eeff6agoIDc3FyWLVt2XnGfTWW/m/OJ90zbHDt2jI0bNwLw/fff07FjxzLbVvU7S0pKwtPTk5tuuomHHnqIHTt2nPd5uuuS3BfT6/WMGTOGqVOn4nA46N27NxERESxduhSA/v37065dOzZv3sz48eMxmUxlhvQcM2YM77zzDjabjVq1al2U4T5rSutQL/4ZrSfmU+eoQmrHRpTNhnYZ3KIUQgghhFOvXr2YO3cucXFxREVF0b59e9e6Jk2akJeX5xpoBCA6OpqJEydy2223oZTCYDAwdepUatWqVWa/ffr0Yc6cORX227ZtW/r370+/fv0IDw+nTZs2+Pr6Vrnf8hc9W7VqxciRI13J7W233XbGLtZ79uxh0KBBgLN7SNOmTdm5cyf+/v4X9N1UpVWrVgwZMoT+/fuX6S5TVdyJiYln3WdlKvvdnE+8Z9qmcePGzJ8/n6effpoGDRpwzz33lNm2qt9ZTk4OL774IpqmYTQaefnll13b3HXXXbz22muEhoae13lXRVOVdY6/SpTcBbgQ1XHrVSmF4+n7IN25X90TU9GatLqoxxDVR27Hi8pIvRDlSZ04f/n5+Xh5edV0GNXiTN008vLy8Pb2pqCggBEjRjBt2jRXH3Vx9TpTnajsb+Fcuu7IZeQaoGkaWutOOH5bgoZzllxJ9IUQQohr28SJE9m3bx9FRUWMHDlSknxxwdxK9O12O3/++ScHDx6sMLTlP/7xj2oJ7Gr3WUh3Vndty7RN7xK0bR3qljEVRiISQgghxLVjxowZNR2CuMq49TDuu+++y8KFC13DapZ+ifNzwuhPujmA9cEt4GQSJFV8sEYIIYQQQojz5dYV/a1bt/LBBx/g6elZ3fFcM7pG+rP+eD5/Bbfk+uN/Obvv1Imo6bCEEEIIIcRVwq0r+uHh4eTm5lZ3LNeUjnV90KHYGdiQHIOnzJIrhBBCCCEuKreu6P/zn/9k5syZtGnTpkJ3nZ49e1ZLYFc7X7OeVsFmtqVa2WhpTu8Dm1E5WWi+0h1KCCGEEEJcOLcS/d9++409e/aQl5eHyWRyLdc0TRL9C9C1QSDbUpP5K6QlvZM3obZvROvet6bDEkIIIYQQVwG3Ev0lS5bw6quvXtEz0l6OOkf48uGGJLYGRVOgN+G5bR1Ioi+EEEIIIS4CtxL9gIAAgoODqzuWa06Qp4Exjc00nPc+Znsx7NqCKraiGU1n31gIIYQQQogzcCvRHzx4MO+88w7Dhw+v0Ee/ZHphcX6GdorCvsgG2QqsRbBnB7TqUNNhCSGEEKKcunXrnnH9sWPHLlEkQrjHrUT/k08+AWDTpk0V1s2bN+/iRnQN0tp0Qi39DgC1bR2aJPpCCCHEZSU7O5sVK1bQpEmTmg5FCLe5lehLMl+91kZ04uc2wQxNXEX7betRd4yVWXKFEEKIy8jevXvp2LFjTYchxDlxaxz9Eqmpqezbt4/U1NTqiuealOgZwvbAxqwNaQWZ6XBkf02HJIQQQohSlFI1HYIQ58ytK/oZGRm89dZb7Nu3D19fX3JycoiOjubRRx8lKCioumO86nWN9Of/dqazwdIcu6ZD27YeLbJRTYclhBBCXNMSExPJyMigefPmGAynU6b3338fh8PBI488UoPRCXF2bl3R//jjj4mMjGT27Nl89NFHzJ49m/r16/Pxxx9Xd3zXhHr+JsKMdrJNPvztXx+1bX1NhySEEEJc8yIiIti0aRM7duygRYsWruWjR4/mjz/+qMHIhHCPW4n+3r17ufvuu/Hw8ADAw8ODO++8k3379lVrcNcKTdPo0iAQgL+CW8KRA6j0kzUclRBCCCEArFYrZrPZ9Xn69Onk5ORIdx5x2XMr0ff29ubo0aNllh0/fhwvL69qCepa1LUk0Q9piQMNtW1DDUckhBBCiJYtW5Kenu76/PvvvxMeHk6XLl04cuRIDUYmxNm51Ud/6NChvPDCC/Tp04eQkBBOnjzJb7/9xqhRo6o7vmtGI4sHFp2NNHMA+33Dabx9PfQeVNNhCSGEENe0Dh06UFRUBEBmZiY//PADr732GgsXLmTnzp1ERkbWcIRCVM2tRD8uLo7Q0FD++OMPjhw5QmBgII8++igtW7as7viuGTpN45am/vDdHEILUmHPCVRhPpqH3DURQghxbRv2vz1VrhvXKZQBjQMA+CU+k/fXJ1VZ9vs7mp7zsXU6HZ6engAEBATw2muvATB8+PBz3pcQl9pZE32Hw8Gjjz7KG2+8IYl9Nbu+XQT2xSfBVuBcsGsrdOhWozEJIYQQ16KzzYJbGZkZV1xuzpro63Q6dDodxcXFGI3GSxHTNU1r0wmVeBA4NUuuJPpCCCGuce5eiR/QOMB1df9CSdIurgZudd0ZNGgQb775JjfeeCNBQUFlZm2tXbt2tQV3LTrauCM/N8onLD+VgTs2ohx2NJ2+psMSQgghrmnuXOGXxoG43LiV6H/66acAbN++vcK6efPmXdyIrnHJfqH8GH4dDXKOMfD4Wti/Fxo3r+mwhBBCiGtWdnY2K1asoEmTJjUdihDnpMpEPzc3Fx8fH0CS+UupbR0fPJWNg751SfIIos62dWiS6AshhBA1Zu/evXTs2LGmwxDinFU5jv7DDz/sev/CCy9ckmAEGPU6YgKcE3CsC24ps+QKIYQQNaz0xFgxMTGsWrUKgAcffFAmzRKXtSqv6JtMJo4cOUJ4eDgJCQkopSqtzDqdW3NusXXrVmbPno3D4aBv374VhqVSSjF79my2bNmC2Wxm3LhxREVFAc5Gh4eHBzqdDr1ezyuvvHIOp3jl6dKsLqv/SuGvkJYM27IKlXQMLfTcn/4XQgghxPlJTEwkIyOD5s2bYzA406Vjx47Ru3dvli9fTmxsLEqpMs8tCnG5qTLRHzlyJM888wzFxcUA3HrrrZWWc6dbj8Ph4JNPPmHy5MlYLBYmTZpETEwM4eHhrjJbtmwhKSmJd955h/j4eGbNmsVLL73kWj9lyhT8/PzcPrErWYd6ARjXnmCvf33STb5Ytq9HC72xpsMSQgghrhkREREsX74cu91OixYtANixYwddu3Zl7dq1HDx4sEweI8TlqMpEv3///vTt25fMzEwmTJjAG2+8cd4HSUhIIDQ01DVCT7du3diwYUOZP5CNGzcSGxuLpmlER0eTl5dHRkYGgYGB533cK5WnUUdbLysbCjxZH9yC67eth/6S6AshhBCXmtVqxWw2A85BSUaMGIHD4eCjjz6SfvvisnfGUXf0ej0Wi4Vp06YREhJy3gdJT0/HYrG4PlssFuLj4yuUCQ4OLlMmPT3dlehPnToVgH79+hEXF1fpcZYvX87y5csBeOWVV8rs73wZDIaLsp9zNbxzY2p98RVNsg9DUjJBJiM6P/9LHoeoqKbqhLi8Sb0Q5UmdOH/Jycmu7jI1qU2bNqSmprpiOXjwINHR0dSpU4dJkybxwAMPnFecl8O5ictLVXXCbDZf0L8jbtW0OnXqnPcBgEr79pfv03amMi+88AJBQUFkZWXx4osvEhYWRvPmFUeiiYuLK9MISE1NvaC4AYKDgy/Kfs5Vy7o+NFP7IPcEAKmrlqLr0vuSxyEqqqk6IS5vUi9EeVInzl9RURF6fc3PIdO2bVuKioqw2WwAfPDBBzgcDry9vV0XLEvWuctgMJzzNuLqdqY6UVRUVOHfkbCwMLf37d6TtBfIYrGQlpbm+pyWllahS47FYilzIqXLBAUFAeDv70/Hjh1JSEi4BFHXPK1Np9MftsroO0IIIcSlpNPp8PT0rOkwhDhvlyTRb9iwISdOnCAlJQWbzcaaNWuIiYkpU6ZkuCqlFPv27cPLy4vAwEAKCwspKCgAoLCwkO3bt1OvXr1LEXaNK2jRkaV1OvN1ZF/Urs2oUw9GCyGEEEIIcTbn1EnM4XCQlZV1zg/I6vV6xowZw9SpU3E4HPTu3ZuIiAiWLl0KOB/8bdeuHZs3b2b8+PGYTCbGjRsHQFZWFq+//joAdrudHj160LZt23M6/pWqMDiMD6NHYHTYGHJ0Nd77dkKLdjUdlhBCCCGEuAJoyo2ZHvLy8pg1axZ//fUXBoOBuXPnsnHjRhISEqocdvNycPz48QveR033sXzqf+vZgx9P7JpL95b10N3+jxqLRTjVdJ0QlyepF6I8qRPnLz8/Hy8vr5oOo1pIH31R3pnqRGV/Cxe9j/7HH3+Ml5cX77//vuup4OjoaNasWeP2gcT56RLu/OWWzJIrM/AJIYS42sn/dUI4XejfgluJ/o4dOxg9enSZLjt+fn5kZWVd0MHF2XVp0xCAjZZmFGekw9FDNRuQEEIIUc10Op1c9RbXPJvNhk53YY/TutVH38vLi5ycnDKJfmpq6jU5mdWlVifAk/qOLA4Z/Nke2JiYbevQIhrUdFhCCCFEtfHw8KCwsJCioqIKw3Ff6cxmM0VFRTUdhriMVFYnlFLodDo8PDwuaN9uJfp9+/Zl+vTp3Hrrra5Rcf7v//6Pfv36XdDBhXu6BOs5lA5/hbSkw7YNcMPl+1yEEEIIcaE0Tbtqh7WUZzdEedVZJ9y6HzBs2DC6du3KJ598gt1u54MPPiAmJoZBgwZVS1CirK7tGtI6I4HmmQfgUDwqM+3sGwkhhBBCiGuaW1f0NU1j8ODBDB48uLrjEZWoHxrIf6zrIHkbAGr7BrTY62s4KiGEEEIIcTlz64r+woULK8xGm5CQwPfff18tQYmKtNYdXe+VzJIrhBBCCCHOwq1Ef8mSJYSHh5dZFh4ezpIlS6olKFGJ1h3Z4xfJV/X7ofZsRxUV1nREQgghhBDiMuZWom+z2Vzj55cwGAxYrdZqCUpUIiSU11vdw9f1+5HgUQv+3lrTEQkhhBBCiMuYW4l+VFQUv/zyS5llS5cuJSoqqlqCEhXpNI0unnnAqcmzpPuOEEIIIYQ4A7cexr3nnnt48cUXWbVqFbVr1yY5OZnMzEyeffbZ6o5PlNK1cW2W/A1rQ1pxx/aP0BwOtAucSEEIIYQQQlyd3Er0IyIiePvtt9m4cSPp6el07tyZDh06XPAg/uLcNG/dGN8d2zjhFUKi3YP6B/dBw6Y1HZYQQgghhLgMuZXog3OWuh49elRnLOIsDAY9nXQZ/IoX60JaELltPZok+kIIIYQQohJuJfopKSn83//9H4cOHaKwsOxoLx988EG1BCYq17V+AL8egb+CWzFy23cw4u6aDkkIIYQQQlyG3Er03377bWrXrs3dd9+N2Wyu7pjEGbTu0Jy6ezbSJPsw9hNH0Z1MQgsJremwhBBCCCHEZcatRP/o0aO88MIL6OTBzxpn9vLkncLf0OI3AKC2rUeLG1rDUQkhhBBCiMuNW5l7s2bNOHToUDWHItyla9PJ9V5tk2E2hRBCCCFERW5d0Q8JCWHq1Kl06tSJgICAMutGjRpVHXGJM9BadyTL6MUGS3M6HNxHUF4umrdPTYclhBBCCCEuI24l+kVFRXTo0AG73U5aWlp1xyTOQgsIYka7e9ngVZ9/7FvA9Ts3oXXuWdNhCSGEEEKIy4hbif64ceOqOw5xjrpYdGwogL+CW3L99g0gib4QQgghhCjF7XH0AQoKCsjJyUEp5VpWu3btix6UOLuO7Rqh+7OAnQENyd78Hf42G5rhnH6dQgghhBDiKub2qDvvvPMOhw8frrBu3rx5Fz0ocXZ+9RvQculStvtEstE7kr7xu6BZm5oOSwghhBBCXCbcGnVn1qxZtGjRgk8//RQvLy9mz55Nv379ePjhh6s7PlEFTdPo4mMFYF1ISxl9RwghhBBClOFWon/48GHuuOMOvL29UUrh5eXFnXfeKVfza1iXluEAbA1sQsH2zWW6VAkhhBBCiGubW113jEYjdrsdg8GAr68vqampeHt7k5ub6/aBtm7dyuzZs3E4HPTt25fhw4eXWa+UYvbs2WzZsgWz2cy4ceOIiopyrXc4HDz99NMEBQXx9NNPu33cq1lQi5Y0XbMCncNOZk4BXscToW69mg5LCCGEEEJcBtxK9Js2bcratWvp1asXXbp04aWXXsJoNNKiRQu3DuJwOPjkk0+YPHkyFouFSZMmERMTQ3h4uKvMli1bSEpK4p133iE+Pp5Zs2bx0ksvudYvWbKEunXrUlBQcI6nePXSDEZe0G1Hv3k1AGrbOjRJ9IUQQgghBG523Xn88cfp1asXALfddhvDhw+nb9++PProo24dJCEhgdDQUGrXro3BYKBbt25s2LChTJmNGzcSGxuLpmlER0eTl5dHRkYGAGlpaWzevJm+ffuew6ldGwxtOrreSz99IYQQQghRwq0r+osWLWLo0KEA6HQ6YmNjAfjhhx+44YYbzrp9eno6FovF9dlisRAfH1+hTHBwcJky6enpBAYG8tlnn3HnnXee9Wr+8uXLWb58OQCvvPJKmf2dL4PBcFH2U10cPfuTMvttErzr4JmcSVuDDn1AUE2HdVW73OuEqBlSL0R5UidEZaReiPKqs064leh/++23rkS//HJ3Ev3KHhLVNM2tMps2bcLf35+oqCh27dp1xuPExcURFxfn+pyamnrW2M4mODj4ouynOn3f5mY+9+9A3PF1RPz2C7oe/Wo6pKvalVAnxKUn9UKUJ3VCVEbqhSjvXOtEWFiY22XPmOjv3LkTcPaxL3lfIjk5GU9PT7cOYrFYSEtLc31OS0sjMDCwQpnSJ1lS5q+//mLjxo1s2bIFq9VKQUEB77zzDuPHj3fr2NeC9vWD+DwD1ge34B/bfsEkib4QQgghxDXvjIn+Bx98AIDVanW9B+eVdn9/f8aMGePWQRo2bMiJEydISUkhKCiINWvWVEjUY2Ji+Pnnn+nevTvx8fF4eXkRGBjI7bffzu233w7Arl27WLx4sST55US2b02dxQc54RXMnn0ZtLIWoZnMNR2WEEIIIYSoQWdM9GfMmAHAe++9xyOPPHLeB9Hr9YwZM4apU6ficDjo3bs3ERERLF26FID+/fvTrl07Nm/ezPjx4zGZTIwbN+68j3et0YXWpXPBShZ6BfNXQFNa7dkOrTuefUMhhBBCCHHV0tR5zLK0c+dO9Ho9zZo1q46YLprjx49f8D6ulL50e77+hqeKW2IpzORj0yb0d8msxdXlSqkT4tKSeiHKkzohKiP1QpRXnX303Rpec8qUKezZsweAhQsX8vbbb/PWW2+xYMECtw8kqlfjds0JKsokzSOA+PhElMNR0yEJIYQQQoga5Fain5iYSHR0NAC//vorU6ZMYerUqSxbtqxagxPu0zVsQufMePysuaQV6+Dw/poOSQghhBBC1CC3htcs6d2TlJQE4JrRNi8vr5rCEudK0+m5IyCLMWteQI9CbV+P1qBxTYclhBBCCCFqiFtX9Js0acKnn37K3Llz6djR+ZBnUlISvr6+1RqcODc+bdujx9koU1tlllwhhBBCiGuZW4n+ww8/jJeXF5GRkdxyyy2A80HXQYMGVWtw4hw1bwcGA3kGDw5n5KPSUmo6IiGEEEIIUUPc6rrj6+vrGsu+RPv27aslIHH+NA9P9rbsw7N+vYjIS+aNbevR+px95mIhhBBCCHH1qTLRX7BgASNGjABg3rx5Ve5g1KhRFz8qcd6imjfEcMTOQd+6JO1YRJgk+kIIIYQQ16Qqu+6kpaWVeV/VS1xezG060iH9bwBW55hxLF8kQ20KIYQQQlyDqryi/8ADD7jeyyy1Vw4tKJjrdGn8Cfxf/X6Y/vyRIVvXoR/9KJqlVk2HJ4QQQgghLpEqE/3k5GS3dlC7du2LFoy4ODrfNoLbvlrK/1k681mjIRw7/hcPPP8oxpGj0Xr0Q9O0mg5RCCGEEEJUsyoT/fHjx7u1gzP13xc1Q1erDqPG3U7d737hnYJI9vnVo9hajOHz91Bb16G762G0gKCaDlMIIYQQQlSjKhP90gn8ypUr2bFjByNHjiQkJISTJ0/yzTff0KpVq0sSpDh3msHIdSNvoNbWPQQs/AlPu9W5YvsGHP/5J9odY9F17FGzQQohhBBCiGrj1jj68+bN46GHHqJOnToYDAbq1KnDgw8+yFdffVXd8YkL1KRtU2pNehGt7xAU8EmjofytD0J9NA3HR6+hcrNrOkQhhBBCCFEN3BpHXylFSkoK4eHhrmUnT57EIaO5XBE0sxnt1gdYFdGVHw8Z+CWsCw/vmU/PDatR+3aiu/sRtNYdazpMIYQQQghxEbmV6A8ePJj//ve/9OrVi+DgYFJTU/n9998ZPHhwdccnLqLrurZgr+4YSw7k8nbz2zjuFcKth5biePcF50O6t9yH5ulV02EKIYQQQoiLwK1Ef+jQodSrV4+1a9dy6NAhAgICGDt2LG3btq3m8MTFpNdp/KNrOOFBGczamMT8+nEc9wrmkT1fY/5jGervbehGP4rWRJ69EEIIIYS40rmV6AO0bdtWEvurxOAmgYT6GHntj2P8WastJ82BPLNzNn5pKThe/zda3yFoI+5GM5lrOlQhhBBCCHGe3HoYt7R77rmnOuIQl1iHuj68OqA+tbwNOGqFYTabXOvUr4txvDABdXBfDUYohBBCCCEuxDkn+kqp6ohD1IDIADOvDajP5MHN8JzyJrTsgOu3m3QMxysTcSz8AmUrrskwhRBCCCHEeTjnRF9cXQI8DQR6GtACLPDPZ3lz0BSWRPZ0rnQ4UD9+jeOlJ1BHD9VonEIIIYQQ4tycc6L/xhtvVEcc4jKwI7mAP/K9mdVgMB/HjMGunaoeiQdxTH0cx0/fohz2mg1SCCGEEEK4pcpEPzk5udKX3W4v81lcPdrW8eaxbnUw6DR+8mnKy/0mk2/2ca602VAL5uCYNgmVfLxmAxVCCCGEEGdV5ag748ePd2sH8+bNu2jBiJrXq4E/tbyNvLzqGJuLfHgmbgrP7PmSWvu3OAvs34Pjv4+i3XwvWs+BaDrp/SWEEEIIcTmqMtEvncCvXLmSHTt2MHLkSEJCQjh58iTffPMNrVrJeOtXo+a1vJg2IJIXfzvKkWwrTzW6kxebtaXuT5+D3Q7WItSXH6K2/IXu3vFoQSE1HbIQQgghhCjHrcux8+bN46GHHqJOnToYDAbq1KnDgw8+yFdffVXd8YkaUsfXxKsDImkd6kVdPxN1bhiGbtLrEFbvdKG/t+H4zz9xrPlVRmMSQgghhLjMuDVhllKKlJQUwsPDXctOnjyJw+Fw+0Bbt25l9uzZOBwO+vbty/DhwyscY/bs2WzZsgWz2cy4ceOIiorCarUyZcoUbDYbdrudLl26cMstt7h9XHH+fEx6pvSOoNDmwKjXQWRDbJOmY1j8f7DsO1AKCvJRs992Xt2/axyaX2BNhy2EEEIIIXAz0R88eDD//e9/6dWrF8HBwaSmpvL7778zePBgtw7icDj45JNPmDx5MhaLhUmTJhETE1Om4bBlyxaSkpJ45513iI+PZ9asWbz00ksYjUamTJmCh4cHNpuN5557jrZt2xIdHX1+ZyzOiUGn4WPSA2B3KF5dm4Jv3f48/ERn9J+9CSeTnAW3rsOR8De6O8ehdehWgxELIYQQQghws+vO0KFDGTduHFlZWWzcuJHMzEzGjh3LsGHD3DpIQkICoaGh1K5dG4PBQLdu3diwYUOZMhs3biQ2NhZN04iOjiYvL4+MjAw0TcPDwwMAu92O3W5H07RzPE1xMSRmFbErJZ/fDmYz5ZAXuU+/idZr0OkCudk4Zr6CY9Z0VF5ujcUphBBCCCHcvKIP0LZtW9q2bXteB0lPT8disbg+WywW4uPjK5QJDg4uUyY9PZ3AwEAcDgdPPfUUSUlJDBgwgMaNG1d6nOXLl7N8+XIAXnnllTL7O18Gg+Gi7OdqEBwMH/gHMHHxbnafLODpVSd5/a7HCe3Zn+z3XsKRlgKAWvc7WvwufB9+BnP7LjUc9cUndUJURuqFKE/qhKiM1AtRXnXWCbcS/eLiYr755hv+/PNPcnJymDNnDtu2bePEiRNcf/31Z92+sgc1y1+VP1MZnU7Ha6+9Rl5eHq+//jpHjhyhXr16FcrHxcURFxfn+pyamnrW2M6mpKuScArSwav9Ipj6+zH2pxfywFdbeSq2Lq2fewvtq49Ra1cC4EhPJfOFx9Fir0e75T40s7mGI794pE6Iyki9EOVJnRCVkXohyjvXOhEWFuZ2Wbe67syZM4fExETGjx/vSr4jIiJYunSpWwexWCykpaW5PqelpREYGFihTOmTrKyMt7c3zZs3Z+vWrW4dV1QPi5eRl/rVo0uED3nFDp5fkcjuHB26MY+hG/cM+Pq7yqpVP+N45UlUikyyJYQQQghxKbmV6K9fv57x48cTHR3tSvSDgoJIT0936yANGzbkxIkTpKSkYLPZWLNmDTExMWXKxMTEsGrVKpRS7Nu3Dy8vLwIDA8nOziYvLw8Aq9XKjh07qFu37rmco6gGHgYdT11XlxHNg2hZ24smIZ4AaO26oPvPu9CuVJedo4dwvPgv1NZ1NROsEEIIIcQ1yK2uOwaDocJQmtnZ2fj6+rp1EL1ez5gxY5g6dSoOh4PevXuXuSPQv39/2rVrx+bNmxk/fjwmk4lx48YBkJGRwYwZM3A4HCil6Nq1Kx06dDiXcxTVRKdp3NOuFjaHwqBzNgBzrXb0nn54jJ2EWr0U9X8fgs0GBXk4ZkxFG3gz2vA70HT6Go5eCCGEEOLq5lai36VLF9577z3uvfdewJl8f/bZZ3Tr5v4wiu3bt6d9+/ZllvXv39/1XtM07r///grbRUZGMm3aNLePIy69kiTf5lC8uuoYOVY7k3uFExw7AFUvCsfMV6HkQd2fvkEdikf3wBNopbr4CCGEEEKIi8utrju33347tWrV4l//+hf5+fmMHz+ewMBARo4cWd3xiStIVqGN1PxiDmYU8eTPh0lIK0Sr3xjd5DegRbvTBf/ehuOFx1AH9tZcsEIIIYQQVzlNVTbcTSkOh4P58+czYsQIjEajq8vOlTCW/fHjF/4AqDwdf26yi+y8uuooO1MKMOs1HulShx6RvmjKgVo8D/XDV6cL6w1oo+5H6zXwiqhPJaROiMpIvRDlSZ0QlZF6Icqr0VF3dDodv/zyC3q9s0+1n5/fFZWUiUvLz6znP33q0SfKnyK7Yvqfx/nnDwf562g+umG3oxv/HHh5OwvbbagvZ6I+fRNVVFSzgQshhBBCXGXc6rrTs2dPli1bVt2xiKuEUa8xvkso/+hYG4uXgaPZVvKL7QBorWLQ/v0GRDRwlVd//Ybj5SdkCE4hhBBCiIvIrYdxExIS+Pnnn1m0aBEWi6XMFf3nn3++2oITVy5N0xgUHUj/RgH8lZhDp3Af17rZiToyej3BDQd/pfEfC5wLjx3G8eK/0I2ZgNa2cw1FLYQQQghx9XAr0e/bty99+/at7ljEVcig0+gR6ef6bLU7+PVAFnlWB6sNXYju344bti+gS/I2DDIEpxBCCCHEReNWot+rV69qDkNcK0x6HW8PasCSfRksTchkn9XMG01vwxI1mIGJf9D/xDp8ZAhOIYQQQogL5laiD5CZmUlCQgI5OTmUHqinT58+1RKYuHqFeBu5p10tRrUKZuWBLBbvzeBYth9fNBxEm4x4fHKPuYbg1D30FFpUk5oOWQghhBDiiuNWor9+/Xreffdd6tSpQ2JiIhERESQmJtK0aVNJ9MV58zDoGBgdyIDGAWw9kce2E7k08rnONQTnFwExtPzoE9r274Wu95U1BKcQQgghRE1zK9GfN28e48aNo2vXrowePZpp06axcuVKEhMTqzs+cQ3QaRrtw3xoH+YDHW5HRUWT8OX/WBDZhwVAREISNxz5hl633ICHl2dNhyuEEEIIcUVwa3jN1NRUunbtWmZZz549WbVqVbUEJa5tWqsYav/zSe5I+4ugoiwSvUP5wNiK+7/Zw9w1B0jLL67pEIUQQgghLntuJfp+fn5kZmYCEBISwr59+0hOTsbhcFRnbOIa5hcWxsiHbmOmbh0Tdn9Jw+xEcvSefHPQyj+/j8dql7onhBBCCHEmbg+vuWfPHrp06cLgwYN5/vnn0TSNG264obrjE9cwzWTGfO94eq5eynVfzmSvVxg/hPcg0JqN4fsNqGF3YkNj47E8OoX7oNdJH34hhBBCiBJuJfrDhw93ve/ZsyctWrSgsLCQ8PDw6opLCBfddf3RIhrQdOarNN39PxSgEkAdjGf19Q/z9tYsQrwMDG4SSL9GAfiYZPx9IYQQQgi3uu6UFxwcLEm+uKS0+o3RTX4DWrTDdd1+z3aMC+cQ5gEn8218tuUk932XwEcbkjieba3JcIUQQgghapxbV/THjh1b5boPPvjgogUjxJloPn7oxj+HWjzPNQRnt0Nr6JK4gS3DHmGxsSHbk/P5cV8mS/ZlckOTQO6PqV3DUQshhBBC1Ay3Ev1//vOfZT5nZGSwZMkSunfvXi1BCVEVTadHG+YcgtMxazrk56GzF9NhwZvEdOnF4SEP8MOBXH4/mE29ALNru6PZReQU2okO9pS+/EIIIYS4JriV6Ddv3rzCshYtWjB16lQGDRp00YMS4my0VjHoJr+JY+YrcOQAAOqv36iXeJBHxk7i7rYN8TCc7pn2875MFu/NwNuoo3WoN+3DvGlXx5sQb2NNnYIQQgghRLU6rz76AAaDgZSUlIsZixDnRAsJRffUq2jd404vPHYYx9TH8duzEXOpRD/Q00CYr4m8YgdrE3OYsS6J+xfu5+HFB/hud1oNRC+EEEIIUb3cnhm3tKKiIrZs2UK7du2qJSgh3KWZzGj3jsfRsCnqyw/BVgwF+ThmvIQ28Ca0YXei6fXc1MLCTS0sJOda2Xw8jy0n8tielM/RbCup+TbX/tLyi/njcA7twryJ8DOhadLNRwghhBBXJrcS/bS0slc8zWYzN9xwA7GxsdUSlBDnSnddf1REAxwzX4U0550m9dO3qIPx6B54As0vAIDaPiYGRpsYGB2IzaHYe7IAf4/Tw3FuOp7Hp5tTYDNYvAy0q+Ps5tOmtjc+Zhm2UwghhBBXDk0ppWo6iOpy/PjxC95HcHAwqampFyEacSmo3GznQ7q7tpxeGBiM1qEb+AWCX4Az6fcPAL8A8PFHM5xu7+5KzmdpQiZbkvLIKrS7lus0aFHLixf6RhASEiJ1QlQg/1aI8qROiMpIvRDlnWudCAsLc7usW1f0d+7c6dbOWrZs6faBhagOlQ3BSUYqavkiV5kKLVsfX1cjoJlfAM38AnH4BnAoMIQtKoAtBWb2ZCt0KFdXHrtD8d66E7So5UW7Ot5YvOShXiGEEEJcXtxK9D/44APS09PRNA1fX19ycnJQSmGxWCi5IaBpGu+99161BiuEO8oOwfkG5OeeeYPcHOfr+BFXI0ADGpx6jQAK9GayjN7Yf7SRGmhhT0ADVliuZ8WBbAAijcW081e0C/WkeXggRv+AMncKhBBCCCEuNbcykb59+5Kbm8uoUaMwm80UFRUxb948fH19ufHGG9060NatW5k9ezYOh4O+ffsyfPjwMuuVUsyePZstW7ZgNpsZN24cUVFRpKamMmPGDDIzM9E0jbi4OBnSU7hFaxWD7sWZqF2bIDMdsjMhOxN16ifZmZCbDW70XvO0F+FpLwLAnptNaNJJ7q9VyNbAaHYGNuQwZg6nwsJUO6Ztx3lnw7+oZbA7uweVdBcKCIIACwRY0AKCXJ81o9wNEEIIIcTF51ai/+OPP/Lhhx9iOHWF0mw2c/vtt/OPf/zDrUTf4XDwySefMHnyZCwWC5MmTSImJobw8HBXmS1btpCUlMQ777xDfHw8s2bN4qWXXkKv13PXXXcRFRVFQUEBTz/9NK1bty6zrRBV0Xz90Lr0rnK9stshJ6tUIyDjdCMgKxOVk1lpoyCgOJdBx9Yw6NgaijU9e/zrsyUomq1BTcg2ehFSmOE8QG42rwdch09mAc0OHaRh7p/UyU9FX7oDkY+fswEQWNIAKPc+wAI+vjICkBBCCCHOiVuJvoeHBwkJCTRt2tS1bP/+/ZjN5jNsdVpCQgKhoaHUrl0bgG7durFhw4YyyfrGjRuJjY1F0zSio6PJy8sjIyODwMBAAgMDAfD09KRu3bqkp6dLoi8uCk2vP3VlPcj5+QxlSxoFAXrIPHzQdWfAlJ1J66xMWuUc5+4TuynIyXUm5UqRY/BibUgrlKZjaVgXADzsRTTIOU5U7jF6J20kKve4sxFx9GCZ5wfK3GcwGE/fAQi0nI45wIJ2qmFAQBCa0XSRvyEhhBBCXKncSvRHjRrFSy+9RIcOHbBYLKSlpbF582buu+8+tw6Snp6OxWJxfbZYLMTHx1coExwcXKZMenq6K8kHSElJ4eDBgzRq1KjS4yxfvpzly5cD8Morr5TZ3/kyGAwXZT/iKlG7NgaDAWOD6DMWU3YbjqxMAjLTmJGYzsYTeezJshNfZOCk3szfAQ34O6ABLbIOOhN94K/glmwPbERUzjEa5h4lPC8Fozo18o+tGFKTITW5wsPEpT9rvv7oLSHogkLQBwWjCwpBZwlGHxSCzhKCPiQUnY/fxfs+hIv8WyHKkzohKiP1QpRXnXXCrUQ/NjaWqKgo/vrrLzIyMqhbty433XST21fVKxvBs3w3hLOVKSwsZPr06dx77714eXlVepy4uDji4k7Pknoxhq+SYbBEeedUJ/ws1G1hoW4LGHZqUVahjf3phRxIL6LpsOfQFedCZhqbdhfwa+bpu2QGZSeyMJWo7ESap8XTM2VL5ccoReVkYcvJgkMJVRcKCIKwSLTwSKgbiVa3PoRFyN2ACyT/VojypE6Iyki9EOXV+PCaAOHh4dx8880AWK1WdDqd2wcpuQtQIi0trcyV+pIypU+ydBmbzcb06dO57rrr6Ny5s9vHFeJy5O9hoH2YD+3DfE4tMUOghet9CwhPyT/VCCjkeA7s96zNfs/apLTqQe9WD0BGGkXp6Xx8wkxUURpRuUeJTD+EOeMkZKWDw3H2ADLTITMdtdvZcFAAmg5q13El/lpdZyOAkNpoOpkoTAghhLgSuZXof/7553Tr1o1GjRqxefNmpk+fjqZpTJgwgZiYmLNu37BhQ06cOEFKSgpBQUGsWbOG8ePHlykTExPDzz//TPfu3YmPj8fLy4vAwECUUsycOZO6detyww03nN9ZCnEFiA72JDrY0/U5z2rnQIbzyn+QpwEt1A9Cw0lMK2D5ocNAEPg0RucLEa3NRAWaaOipuM4nH/+8dFRGOmSmOZP6zDTISHN2/ym2Vjy4ckDSMUg6htq05nR3IJMJ6tQ7dfX/VAMgPBLNL7DiPoQQQghxWXEr0f/jjz8YNWoUAN988w3//Oc/8fLyYs6cOW4l+nq9njFjxjB16lQcDge9e/cmIiKCpUuXAtC/f3/atWvH5s2bGT9+PCaTiXHjxgGwd+9eVq1aRb169XjyyScBuO2222jfvv15nbAQVwpvk55Wtb1pVdu7zPIgTwP3d6jFgYxC9qcVkZhdxOFM52slEDM0igDfaDRgWUIm+cUOGgZ50CDQjJcBSDkBxw6jjh5GHTsExw7DyaTKhxm1WuFwAuqwsyuQq4Sv/6mr/5Gnf4bVQ/PwrLgPIYQQQtQItxL9oqIizGYzOTk5JCcn06WLc/SQc+lP1L59+wrJef/+/V3vNU3j/vvvr7Bd06ZN+frrr90+jhBXO4uXkSFNg1yfi2wODmUWsT+9kMOZRdT2OT0u/0/xGexPL3J9DvUxUtfPRJhvI9rFtKHDMGf3IVVUCCcSUccOQ+kGQHZm5UHkZMGe7ag9253blywPCS3VAKjvvBNQK8w5upEQQgghLim3Ev2wsDBWr15NUlISrVu3BiA7OxuTSR7eE6KmmQ06mgR70iS44tX0QdGB7EstZH96IYcyi0jKLSYpt5hN5GHQaXSo60z043MUM3YZCPNrRliTNtTtaCLM10QdXSF+KUecDYDjR1BHD8HxI1BUWHkwJ5PgZBJq6zrgVAPAYIDQiLIP/waFOB8K9vKW+QGEEEKIauJWon/ffffx2WefYTAYeOihhwDYtm2bK+kXQlye4hoGENfQ+b7YrjiRa+V4tvNVumGQmFXEoUznqzxfsxczbhiEv4fzn4s9KXmYcjMJy0zEdPywsxvQscOQfKzyh4FtNuccAUcPAuXmBzCawD/QOQeA/6m5AU791AKCXOvwlAaBEEIIca40Vdm4lleJ48ePX/A+ZBgsUd7VWCcKih0czS5yNgJyrBzPLuZYjpVj2VaUUswbFe1KtCcsOcjBDGeDINjLQJifibq+Jup462muZdMw+8ip7j/ORgAZF+G7MpmcDQD/cg2A8g0ET68aaxBcjfVCXBipE6IyUi9EeZfF8JpCiKuXp1FHY4snjS1lu/8opcgpspdJnsP9TFjtiqQcK6n5NlLzbWxPygfgpuZBNO7SG4CEtELm7UwlzAPCbNnUyUmibtpB/E8cQMs8NSJQVV2AyrNaT3cLKreqzGeT+fQdggDL6QaBfxCafyAEnJpV2MNT7hAIIYS46kmiL4SokqZp+HmU/WfiiR51AbA7FCl5xRxz3QWw0rL26cnsDmYUsv5o7qlPOiAMCMMj4jpqNTUwbUB9POyFkJnBjsQMdHnZBBekEZR9EkNWGiorwznmf1b6OTQIitxvENSqgxbZECIbodVrCBEN0ExmhBBCiKuFJPpCiPOi12nU8TVRx7fyh/Lbh3kz8bowjmdbXY2BY9lWcq0OUvKK8TBoaEYvCPVi1kYrh7MMQBA6rTGBIQZCIo3U8jbSOcKH7rUMkJVOcXo6RRkZeOWko2U5GwEqK901CRjWis8YVMpaBEcPOR8u/vNXZyNAp3MOERrZECIbO3+G15fkXwghxBVLEn0hRLWweBnpXs9YYXlOkZ2MQluZrjONgz3wMGqczLORUWAjLd/52pNaQKivkR6RfuDpxW4VyJQtiXga6hDibSCklpEQbyMhXkaCvQx0DtbhkZcFmWnOBkDJXYHMUg2CrHRnV6DyHI4zJP+NnFf+I09d+TfKiGNCCCEuf1Um+itWrHBrB3369LlowQghrn6+Zj2+5rLj6v+zSx3X+2K7Ir2gmJS8Yk7m2agfcPqKel6xHQ+DRoHNwZEsK0eyyibs/xvZGM3PF+qE887a4xynmFp1jQRHG0o1CPTU0hXjlXzEORHY4f3On8nHKgZbJvlf7kz+9XrnbMGRDaF+I2cjILz+xfuChBBCiIukykR/9erVrvdKKfbu3UtAQAAWi4W0tDQyMzNp2rSpJPpCiIvKqNeo7WOitk/Fq+bd6/nRLcKXXKuDk3nFnMwvJjXPxsm8YjIKbXgbda6y8WmFJGZZ2ZNaUGE/faP8Gd+1JVqTlpzIsTJny0kCjYrAwiwCslMITD1CwPH9BCYl4G/NQ1e6h7/dfnq40FLJf1pEFI7w+hDZ0JX8y5V/IYQQNanKRH/KlCmu959++ikdO3Zk8ODBrmVLliwhKSmpeqMTQohyNE1z3RWICvKostxzvSJO3RUo2yA4mV9MXb/TCXhSbjFrE3NOfdIDdcCjDkR1hih4t0Ux4SnOK/+Lcvw47PAk0JpNgDWHQGsOgUXZzp9HDmE+FA9/LDt95b9Mt59GEB4pyb8QQohLxq0++qtXr+aTTz4ps+z666/nvvvuY8yYMdUSmBBCXIhaPkZq+VR8RqC8+gFm/tU9jMxC5/MBrlehnYwCG0HNGqNr2wqALSsS2Xoir9L9tEvbw7M7PgUgz+DBzOgRzsbA4RwC49cTaF1OoC2PgCB//OpFoKvX0DlbsNkTjEYwmk/9NDl/6g0yBKgQQogL4laiHxAQwMaNG+nUqZNr2caNG/Hz86u2wIQQ4lII9DQQW9+9f8tGtrDQLcKXjPKNgoJiwuu2RGsyGg4nkJaUwZ+12la5n+e2fUzb1UtRwK+hMewMaIhfcR6+xXn4u37mE+goINSRd7oBYDA6hwYt89nkvEtQ8tlock4wVvK+pOFgLFXOWGqdyQSW2mjGszeKhBBCXFncSvRHjx7N9OnTWbRoERaLhdTUVI4ePcrjjz9e3fEJIcRlo2VtrzJzBZTmnNmwkfN9kZ3HDqWRcSKFjPQsMnIKyChykIGZTJMvgdZs13Z7/Ovze2iHSvdZP/c4b2x8C3DOA/B4zAS8bYX4Fec5X4V5+Flz8S3Ooln2IWoVZrjKVnYvoMpp0Mt0M5JnDIQQ4mrhVqLfunVr3n33XbZu3Up6ejrt27enffv2+Pr6Vnd8QghxxfE16+nVpBY0qVVmucrPhSMHUHUGOkf7SU2mv+0IzY5nko2RbJ2ZHJ0HWXpPcgyehOWfdG2br/fgsE/V057/8++vXIn+T3W78UXUQPysZe8S+BXn4W/NZUTib67tTpoDMDmK8Tl6GH3iwbLPGNSphxYZJZOKCSHEFcrtcfT9/Pxo3rw56enpREdHV2dMQghxVdK8fKBpa7SmrV3Lmp56lafsdrAVQ/EwKC7Gq6iQt7OsZBcUk1VoI7vIRrbVQbZVkV0M4T1j0XQdoNhKTo6FQquZQk8zKZ5BZfbr6yhihGcqFFuh2Mqz9e4gxeQPgKetEB9bPj7FBfjY8umVtInef/4Kf/5KijmALZam+Ph64RtswadObXzC6+EbGYmXt4c8TyCEEJchtxL91NRU3n77bQ4dOgTA3Llz+euvv9i6dSsPPfRQdcYnhBDXJE2vd15VNztHFjIA9UPd2/Y2pRhuc5BVaCe7yE5Okf1U48AOgL75S66y3ksO4ptbTG6xnQKDBwUGD06eGsyoVUaCq9x+37rMjB5x+iCpp15bD6NTDj62/kZQZARaZEPm5QRxLN+Bj0mHj0mPj8k5SpK3SUeoj4nIALkrIIQQl4Jbif5HH31Eu3bteP7557nvvvsAZ3eezz//vFqDE0IIce40TcPLqMfLqKfOWXpYvjWoAQAOpcgvdpBbZCfX6iAnJ4/aLfuhJTeAw/uxpOTQ98R6cg2e5Bm8yDF6kmfwJNfoRaHejNe65ai1NhSwtd1Y/vZvUOnxekT68mSPugAk5Vj5548H8TE5GwFeRj3eRh1eJh3eRj3DmgW5hkI9kF5Ian4x3sbTZb1MOjwNOvQ6uZsghBCVcSvRT0hI4Omnn0anOz0ZjZeXF/n5+dUWmBBCiEtHp2muq+8AWDwAC9AScHYvalKY73zG4PB+OLLV+TPpKMXoMCq7a1+3H/iZVI8Acg2e5Bq8yDV6Od/7BNEwoRBHgQ4tsiE5vnWx2hXpBTbSK85rRt+G/q73SxMy+Sk+s9LYGweaeO06C1id3ZFe3pqHCQfemh0vzY4XNrxUMV6qmGgtlzoqD4qLKbIWY7MW42krQGe1OmdC9vYBHz/w9QMfP7RS7/HxQzPI6ERCiCuHW4m+v78/SUlJhIWdfhDs6NGjBAcHV1tgQgghLi+ahxdEt0SLbulapgoLMCceRB12TiqmDifQIukwZB2scj/q1KuBpvFlaD3y6jUlzz+EPJvzrkK+XSPPAbX+byF2ay4UWwn3bEx7j3DyNSP5OpPzpfcg3+CBdigex3cTACjW9Kzr+fKpI+lPvU6PHjR272+EnlgPwMqwLnwYPQJNc+BpKMLLVohnbhGemUV42Qt5dvtrrtGLfgrrSoHBjKdO4WnQ4Wky4Gk24uVpwuJtJMjX09kQKNUocJhNKIcDrdRFMiGEuJTcSvSHDBnCq6++yvDhw3E4HPzxxx989913DB8+vJrDE0IIcTnTPDyhcXO0xs1dy1RRISSeuvJ/OMH588RRUI6y2yqFx4nDeJw4jOUsxxnIHgZWstyBRrHu9H9lOhTP7JhNnsGDfL2H86fBg3yDJ/l6D+rmp5TZ1sNWRKHB7Fxv8HSt87AXlRmidEl4d455lR1FqcSgg39wf8J8AP72i+S/be7H03YcT/tBPO1FeGLDEweeesVd9n0Ee5vAx49tplBSjb54eXng6e2Fp683nn6+eJhNeJt0+Jj1uAZK1TR54FkIcc7cSvT79OmDj48Pv/76KxaLhVWrVjFq1KgyE2gJIYQQAJrZAxo1R2tULvk/eqjMlX9OJDq7y1wAnQZmgw5MvmA0ozcaiTHmg8kOxmIwFYGhAM2U55wgLLQ5GNuB0cggo4lBpkPYDSYKDGbylYGC/HwK8gooLiiA9t0gNxtys7k+dQtpmpkCnZkCg5kC/amXwYPahemuePINZgr1zldGJfHe+td81KnyvzS/k7W1Ik6tcQA5p17QMiOB/277yLlPvZnxnZ7AbLfiYbfiaS/C49R7s8PKwONraZR7DDSN/d51OeAThqfDioe9CLOjGA9HMZ52Kx6OIoKtOad+SRqnGxEln0990ADdqQfBTWbnz1PvtZL3Zg8wm8FU9r1W8t7sWcl6M5pOf0G/byHEuXEr0Y+Pj6dTp04VEvuEhAQaNWpULYEJIYS4emhmD2jYFK3h6cFEVVERHD2IOnIACvKcs/QaS2b+NaOZSs3eayz1MplPvzcYLvhKtw4wAmeaH3kooBwOKMh3Jv85WZCbjcrNhsiG0DwEcrNpm5vN3CNzKCgoorDIRkGxnfxSDYPAkkQbaJWZgIfDSoHeTL7e2YAoPFUuqNSkaoV6M+lm/0qicup8cqerwbQpMJqvGvSvtJyfNZfP1vzX9fmxmAnkGrzwONVw8LSXNA6sdEvZTtfUnQAkeQSxLrglHjlWTPZ8POyZmB3FmO3FmB1WInNPuJ7RsGp6DMpe6YRtgPN3VqYBUL4RUbHxcHq9s7HgXHbqp8nsakRgNMldDyHKcSvRf/HFF5kzZ06F5VOnTmX27NkXPSghhBBXP81srpD8X840nc75sK63D9R2PrNWPq0s3WgIDg7mZNIJyM1x3RlQOXGu9wNzsxmYc8LZWMjOPl3Objs1vbFz7/7WHD5aO5VCvYkinelUY8BEkd5Eod5EVO4x1/Hr5x6n74n1FOjNrvXOOwwmvIvLPvGcZvYn1+hd6bnWy0tyDp8KHPKpw5xGN1T5vXyy5r8EWnMBmNbybjZZmmG2W50vh/POg8leTJuMeO48+DMUW8k12Phfgx7O9UVWTPnFeDismO1ZmO2ptMja79pnptGHfIMHplONC5OjGKPDhq78XM+adqohUOpV+q6EyYRWVSOhpDFhcpYrs53e4Lx1pNOBpjv1s+rPl1tjQzkc4LCD3QHq1E+H3dk4tNtPvz9bGaXAywf8A8A3wDkEsLjsnTHRd5y6QqCUcr1KJCcno5dfshBCCFElzWCEgCDni4oNA3fogdqn3iulnAmX8xOuXFc5H3HuAnRRp9aVLHf9161Axbref2C1U1isKCx2UGh3UGhzUFCsKLQr6vW8HZ35VigqpE56IUOTbRQV2ym0OSiyKYrsikI7FDnAs0dfNGs+FBViNwUCUHSqIVJa6S5OuQZPfqnbtcpzfm7bxwRa4wFYHHEd39XrXaGMyV5MWMFJ3tj4lutc/9vkDmyaHrOjGNOpRoHZUYwpv5iOibtplbkfgGSPQHb7R51ef+ruREljonZBGvpTX5ziHH9vmnbGhkCK/tSeK6zTnF2mynw+tQ9NK5d4lyTi5Zc5yiXu9rNFe340zfnQuX8g+Aei+QWeeh8A/kGnPjvf4+F52TV+riVnTPRvu+021/tbb721zDqdTseNN97o9oG2bt3K7NmzcTgc9O3bt8KDvEopZs+ezZYtWzCbzYwbN46oqCgA3n//fTZv3oy/vz/Tp093+5hCCCHE1UQrSSIvAj+PM3dXKhEVDlFnLNHB9e55wO5QWO2KIpuDIruDQpvzvaehPjrPYVBUiF9uPg8m5lNktVFodTYiimx25zY2RXCPWDRbG7AW4V0cSh1HHkXosKLDqumxagaseiM2g8l5ldlaCDYbu/0bYC3XwCgRXJTpSvT3+kXybrNRVZ7RF6ufxcteBMCUNv9gr389jA7bqcaDzXVXISbtb247tBSAdJMvc6MGOcs5ik+9Tr23F9P15A4s1mwUcNSrFqlmf8yn9uPcrxWTw4bZbsXbXujGb6YGKeXsvpaT5Xz2pvzq0h9MJnA1BAJPNwL8AtH8g1zv8QtAM7jV0UScgzN+o++99x5KKf7zn//w/PPPu5Zrmoafnx8mU+V/TOU5HA4++eQTJk+ejMViYdKkScTExBAeHu4qs2XLFpKSknjnnXeIj49n1qxZvPSSc/bGXr16cf311zNjxozzOUchhBBCXCJ6nYanTsPTWMWwomYPfP0CGBxW+eryRp56leZQimK7otjRGL2pBwDKbue/J7IpKirCWmilqKgYq9U5X4K12E6Lej3QdO2gqJBaeTp65adRZAerUhQ5NKxKo0g5GxOmiEgoKgBrEUUmT4p1Rop1xjIjMwHUL0gGgxGUgxyTD7+HdqAqDXOOYjn17MXPYV1YEt6j0nLhecm8s+H0Rc37uk4GcDUuSl4mh41Bx/6gS+ouAPb4RfJbaAeMpxoYpcsZcdAvZRN6TQO9jr2+EVj1ZowojJodk6YwojChnKNE6RzOOwl6vfMuQ8kdhtxsyMpw/nSX1Qqpyc4XZRsB5RsIrrsEfgFlGwH+gWinGgp4+Ti/c4MBDEbpQnQWZ0z0Q0JCAHjrrbfQ6XQYSrW0bDYbxcXFGI1nnzwkISGB0NBQatd23nzs1q0bGzZsKJPob9y4kdjYWDRNIzo6mry8PDIyMggMDKR58+akpKRUtXshhBBCXEN0mobZoGEutUzT62kWHujW9i1Ovap2evCR15Si2KGw2hRWh8Jqc2C1O+9aeJui0PveDkBIkZ1Hj+ZgtTucZUpepz6H9H8BnYcOS2AAYRuO0Dq5yLUfq6PkJ/j710H3n3fB4UA57GT8Yasyyu7dW6Gr7w16HceOFLJ0W1aVZa9/dqJrFulPfz5EfFrldw36RPnzaNc6ABzKKOSppUcw6jVMOg2jXsOgO/UeOw83UNQrzkRlZ7D8pMbOAiMGayHGogKMRfkYC3MxFhdhKcoiLmmD6xirarXFoOylGi52DMr5PiQ5Bd9jh1E458Ww6fQYHTb0ylF5FypNB0Zn0o/e4HyY32As0xhwvYzOMprBeHqb8uVOlXG9NxiddxoM5fZr9kALr1/l9325cOseydSpU7njjjuIjo52LTtw4ABffvkl//nPf866fXp6OhbL6VGSLRYL8fHxFcqUnoDLYrGQnp5OYKB7f7RCCCGEEBebpmmY9Bqms1w49jXr6dMw4Kz70wUEMaSdgyHuHFwp5t7sbCw472A472SUfA7zM6F5OS+4Noso4iGTB1ZXOWeDpGS7kiQfoFGQB2aDrkIZq13hZz59ola7otDmoLCKtoYttD5aUGM0YN9fJ/h9f5ZzfjoT4Hu6XJSvjv5Nb0RlZeDIzOCtlOjKdwj8Y++3DDixDoCVoTHMbHITAJpylGkQGB02Zv71CjrlAKuVdxsMI9nTgsFhw3iqEWFw2DEqGy0zEuidvBmAdJMfy+p0OtXQyHOWU3bndg47bTP24WNzPrh+wtNCjsELozrVGDlV1rc4D7OfP/rXLv8BadxK9A8fPkzjxo3LLGvUqBGHDx926yClH+ItUf7BDHfKnM3y5ctZvnw5AK+88spFmbnXYDDIDMCiDKkTojJSL0R5UidEZc61XoS4WS44GNo1dK/s5EHuHT/IolgaVcd5N8PucDYMbCWNAwcNg73xOtUCurmDic4NC0o1RByu7SxeJkLaOO8S2ByKfr/sO13m1L6sNjvWYhvhd4wmwOMWHJlpmI9aMWfaKVYaDk2HVa/DirNho3fY0Wm4+v8c8A3nsE+dSs/DZLe6Ev2THgHMq2IIWoDpG950Jfrf1uvDijodK5R5eM/X9FfHL9rfd3X+W+FWou/t7U1WVhYBAQGuZVlZWZjN5qo3KsVisZCWlub6nJaWVuFKvcViITU19YxlziYuLo64uDjX59L7O1/BwcEXZT/i6iF1QlRG6oUoT+qEqMyVWi/0p14epT7kZ1vJP7U+1AihIVXf9ih9zo90PPNc2DkAkY3p0wb6nFpmdzjvOtjsCtup9/q7vkfZ7WAr5tHUPPIKbc6u5dZTP202bDYHdZt0RDeoDdiKseQ7GJmZg83uwGaHYocDmwPnvh3g1+06NFse2G3UcgTQyJ5BMRo2dBSjw4aGlyUIu9Fw0X6P51onwsLcfMAFNxP9zp078/bbbzN69Ghq165NcnIyc+bMoWvXqofGKq1hw4acOHGClJQUgoKCWLNmDePHjy9TJiYmhp9//pnu3bsTHx+Pl5eXdNsRQgghhBDodZqz+1G5zFXT60Gvp2FdD7f2Uxu484wlurne3XbqVVHVD11fbtxK9G+99VY+//xznnnmGYqLizGZTPTq1avM8JtnotfrGTNmDFOnTsXhcNC7d28iIiJYutQ5JFX//v1p164dmzdvZvz48ZhMJsaNG+fa/q233mL37t3k5OTw0EMPccstt9CnT5+qDieEEEIIIcQ1T1OVdY6vglKKnJwcfH19r4jJD44fP37B+7hSb7GJ6iN1QlRG6oUoT+qEqIzUC1FedXbdqWKQ24qOHj3Kt99+y/z589E0jePHj7v9MK4QQgghhBDi0nIr0V+7di1TpkwhPT2dVatWAVBQUMDnn39ercEJIYQQQgghzo9bffS//vprnn32WerXr8/atWsBiIyM5NChQ9UZmxBCCCGEEOI8uXVFPysri8jIyDLLNE27IvrpCyGEEEIIcS1yK9GPiopyddkp8eeff9KoUaNqCUoIIYQQQghxYdzqujN69GhefPFFVqxYQVFREVOnTuX48eNMnjy5uuMTQgghhBBCnAe3h9csKipi06ZNpKamYrFY6NChAx4e7k1OIIQQQgghhLi03B5e02w207RpU5o3b06zZs2umST/6aefrukQxGVG6oSojNQLUZ7UCVEZqReivOqsE2513UlNTeWdd94hPj4eb29v8vLyaNSoEePHjyckJKTaghNCCCGEEEKcH7eu6M+YMYOoqChmz57NrFmzmD17Ng0bNmTGjBnVHZ8QQgghhBDiPLiV6B84cIA777zT1V3Hw8ODO++8kwMHDlRrcJeDuLi4mg5BXGakTojKSL0Q5UmdEJWReiHKq8464dbDuC+++CI333wzTZs2dS3bu3cv8+fPl5F3hBBCCCGEuAy5leh//PHH/PHHH7Rv3x6LxUJaWhpbtmyhR48e+Pr6usqNGjWqWoMVQgghhBBCuMeth3GLi4vp3LkzANnZ2RiNRjp16oTVaiUtLa1aA6wpW7duZfbs2TgcDvr27cvw4cNrOiRxkb3//vts3rwZf39/pk+fDkBubi5vvvkmJ0+eJCQkhMceewwfHx8AvvvuO1asWIFOp2P06NG0bdsWcHZtmzFjBlarlXbt2jF69Gg0TaO4uJj33nuPAwcO4Ovry4QJE6hVq1ZNna5wQ2pqKjNmzCAzMxNN04iLi2PQoEFSL65hVquVKVOmYLPZsNvtdOnShVtuuUXqhMDhcPD0008TFBTE008/LXVC8PDDD+Ph4YFOp0Ov1/PKK6/UfL1QogK73a4eeeQRlZSUpIqLi9UTTzyhEhMTazoscZHt2rVL7d+/Xz3++OOuZXPnzlXfffedUkqp7777Ts2dO1cppVRiYqJ64oknlNVqVcnJyeqRRx5RdrtdKaXU008/rfbu3ascDoeaOnWq2rx5s1JKqZ9//ll9+OGHSiml/vjjD/XGG29cwrMT5yM9PV3t379fKaVUfn6+Gj9+vEpMTJR6cQ1zOByqoKBAKaVUcXGxmjRpktq7d6/UCaEWL16s3nrrLfXyyy8rpeT/D6HUuHHjVFZWVpllNV0v3HoYd9WqVZU1EPjuu+/OralzhUhISCA0NJTatWtjMBjo1q0bGzZsqOmwxEXWvHlzV6u6xIYNG+jZsycAPXv2dP3eN2zYQLdu3TAajdSqVYvQ0FASEhLIyMigoKCA6OhoNE0jNjbWtc3GjRvp1asXAF26dGHnzp0o9+anEzUkMDCQqKgoADw9Palbty7p6elSL65hmqa5BqKw2+3Y7XY0TZM6cY1LS0tj8+bN9O3b17VM6oSoTE3XC7cS/W+++YY333yT3NxcAJKTk3nuuefYsmXLeZ305S49PR2LxeL6bLFYSE9Pr8GIxKWSlZVFYGAg4Ez6srOzgYp1IigoiPT09DPWldLr9Ho9Xl5e5OTkXKpTERcoJSWFgwcP0qhRI6kX1ziHw8GTTz7J/fffT6tWrWjcuLHUiWvcZ599xp133ommaa5lUicEwNSpU3nqqadYvnw5UPP1wq0++tOmTeOzzz7jiSeeoFevXixbtowbbriBYcOGuXveV5TKWkel/5jFtaeqFvOZWtJSj65chYWFTJ8+nXvvvRcvL68qy0m9uDbodDpee+018vLyeP311zly5EiVZaVOXP02bdqEv78/UVFR7Nq166zlpU5cO1544QWCgoLIyvr/9u4npOk/juP4axKjP6P5X3Cks74eaiGl7hDUoUunDiES5Em8SRASSF7yZIeoSCJDCgJvnlQI9FhGSJCoaGVFa9/+2GiWI2cudG2/gz+//MR+v99Xfz/dnM/HaXzY4L3tzXjts8/e3+9qa2tTcXHx3953s/rC1o7+zp07VVdXJ5fLpd7eXlVVVenMmTPKyrL18C1nebLQsm/fvlnfxpDZ3G63IpGIJCkSiWjv3r2SVvfEzMyMcnNzf9srubm5qx7z69cvzc/PrzoqhPQTj8d148YNnThxwhpCQF9Akvbs2aNDhw5pbGyMntjGXr9+reHhYZ0/f17t7e16/vy5bt26RU/Aev/cbrf8fr/evn2b8r6wldRHRkbU3Nwsn8+na9euKRQKqbW1VeFw2O5z31IOHDigUCikcDiseDyuoaEhVVdXp7osbILq6moNDg5KkgYHB+X3+631oaEhLS4uKhwOKxQKyTAM5eTkaNeuXXrz5o2SyaQeP35s9UpVVZUePXokSXr69Kl8Ph87MmkumUyqs7NTHo9Hp0+fttbpi+1rdnZWP378kLQ0gWdiYkIej4ee2Mbq6urU2dmpjo4ONTU16fDhw7pw4QI9sc39/PlTsVjMuj0+Pq6SkpKU94WtOfqNjY1qbGxURUWFpKXzij09Perv79f9+/fX94qkuZGREXV1dSmRSOjkyZOqqalJdUn4n7W3t+vly5eKRqNyu906e/as/H6/bt68qa9fvyo/P18XL160vi339PTo4cOHysrKUn19vY4ePSpJCgQCunPnjhYWFnTkyBE1NDTI4XBoYWFBt2/fVjAYlMvlUlNTk4qKilL5lPEvXr16pdbWVpWUlFgfnufOnVN5eTl9sU29f/9eHR0dSiQSSiaTOnbsmGpraxWNRukJ6MWLF3rw4IFaWlroiW3uy5cvun79uqSl3fbjx4+rpqYm5X1hK+jPzc399qeBd+/eWRMqAAAAAKQPW3/GdblcGh8f15MnTzQ7O6uWlhYFAgHrJwoAAAAA6cXWGf2BgQHdu3dPxcXFmpyclCQ5nU51d3dvaHEAAAAA1sdW0O/v79fly5dXTNrxeDz6/PnzhhYHAAAAYH1sBf1YLKb8/PwVa/F4XDt22Dr5AwAAAGCT2Qr6Bw8eVF9f34q1gYEB+Xy+jagJAAAAwH9ka+pOJBLR1atXFY1GNTMzo8LCQu3evVuXLl1Sdnb2JpQJAAAAYC1sBX1p6UIygUBA09PTysvLk2EYGXtlXADAP5ubm1NDQ4PKy8t15coVa/3u3btyOp2qr69PXXEAAEk2x2tKksPhkGEYMgxjI+sBAGwBpmkqOztbnz59UiQSUU5OjrV+6tSpFFcHAJBsntEHAOCvTNPU/v37VVFRoeHhYUlLV03/8OGDysrKUlwdAEAi6AMA1sE0TXm9Xvn9fj179kySNDU1pUQiIY/Hk+LqAAASQR8AsA7LQb+yslKTk5OKxWIyTVP79u1j9DIApAmCPgBgTRYXFzU1NSWv1yuXyyXDMDQ6OqpgMCiv15vq8gAAfyLoAwDW5OPHj3I6nSoqKpIk6/iOaZqczweANELQBwCsSTAYVGlpqRwOh6SloM+OPgCkH4I+AGBNls/nLysoKFBBQYHm5+dVWlqausIAACvYvmAWAAAAgK2DHX0AAAAgAxH0AQAAgAxE0AcAAAAyEEEfAAAAyEAEfQAAACADEfQBAACADETQBwAAADIQQR8AAADIQAR9AAAAIAP9AaN9BuxIm6x0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 4)\n",
    "\n",
    "N_Y = 250 #use this many to approximate D(N)\n",
    "N_array = np.arange( 1000, 50000, 2500 ) #use this many samples in the approx. to the variance.\n",
    "D_N_results = np.zeros( len( N_array ) )\n",
    "\n",
    "lambda_ = 4.5 \n",
    "expected_value = lambda_ #for X ~ Poi(lambda) , E[ X ] = lambda\n",
    "\n",
    "def D_N( n ):\n",
    "    \"\"\"\n",
    "    This function approx. D_n, the average variance of using n samples.\n",
    "    \"\"\"\n",
    "    Z = poi( lambda_, (n, N_Y) )\n",
    "    average_Z = Z.mean(axis=0)\n",
    "    return np.sqrt( (  (average_Z - expected_value)**2  ).mean() )\n",
    "    \n",
    "    \n",
    "for i,n in enumerate(N_array):\n",
    "    D_N_results[i] =  D_N(n)\n",
    "\n",
    "\n",
    "plt.xlabel( \"$N$\" )\n",
    "plt.ylabel( \"expected squared-distance from true value\" )\n",
    "plt.plot(N_array, D_N_results, lw = 3, \n",
    "            label=\"expected distance between\\n\\\n",
    "expected value and \\naverage of $N$ random variables.\")\n",
    "plt.plot( N_array, np.sqrt(expected_value)/np.sqrt(N_array), lw = 2, ls = \"--\", \n",
    "        label = r\"$\\frac{\\sqrt{\\lambda}}{\\sqrt{N}}$\" )\n",
    "plt.legend()\n",
    "plt.title( \"How 'fast' is the sample average converging? \" );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the expected distance between our sample average and the actual expected value shrinks as $N$ grows large. But also notice that the *rate* of convergence decreases, that is, we need only 10 000 additional samples to move from 0.020 to 0.015, a difference of 0.005, but *20 000* more samples to again decrease from 0.015  to 0.010, again only a 0.005 decrease.\n",
    "\n",
    "\n",
    "It turns out we can measure this rate of convergence. Above I have plotted a second line, the function $\\sqrt{\\lambda}/\\sqrt{N}$. This was not chosen arbitrarily. In most cases, given a sequence of random variable distributed like $Z$, the rate of convergence to $E[Z]$ of the Law of Large Numbers is \n",
    "\n",
    "$$ \\frac{ \\sqrt{ \\; Var(Z) \\; } }{\\sqrt{N} }$$\n",
    "\n",
    "This is useful to know: for a given large $N$, we know (on average) how far away we are from the estimate. On the other hand, in a Bayesian setting, this can seem like a useless result: Bayesian analysis is OK with uncertainty so what's the *statistical* point of adding extra precise digits? Though drawing samples can be so computationally cheap that having a *larger* $N$ is fine too. \n",
    "\n",
    "### How do we compute $Var(Z)$ though?\n",
    "\n",
    "The variance is simply another expected value that can be approximated! Consider the following, once we have the expected value (by using the Law of Large Numbers to estimate it, denote it $\\mu$), we can estimate the variance:\n",
    "\n",
    "$$ \\frac{1}{N}\\sum_{i=1}^N \\;(Z_i - \\mu)^2 \\rightarrow E[ \\;( Z - \\mu)^2 \\;] = Var( Z )$$\n",
    "\n",
    "### Expected values and probabilities \n",
    "There is an even less explicit relationship between expected value and estimating probabilities. Define the *indicator function*\n",
    "\n",
    "$$\\mathbb{1}_A(x) = \n",
    "\\begin{cases} 1 &  x \\in A \\\\\\\\\n",
    "              0 &  else\n",
    "\\end{cases}\n",
    "$$\n",
    "Then, by the law of large numbers, if we have many samples $X_i$, we can estimate the probability of an event $A$, denoted $P(A)$, by:\n",
    "\n",
    "$$ \\frac{1}{N} \\sum_{i=1}^N \\mathbb{1}_A(X_i) \\rightarrow E[\\mathbb{1}_A(X)] =  P(A) $$\n",
    "\n",
    "Again, this is fairly obvious after a moments thought: the indicator function is only 1 if the event occurs, so we are summing only the times the event occurs and dividing by the total number of trials  (consider how we usually approximate probabilities using frequencies). For example, suppose we wish to estimate the probability that a $Z \\sim Exp(.5)$ is greater than 5, and we have many samples from a $Exp(.5)$ distribution. \n",
    "\n",
    "\n",
    "$$ P( Z > 5 ) =  \\frac{1}{N}\\sum_{i=1}^N \\mathbb{1}_{z > 5 }(Z_i) $$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "N = 10000\n",
    "print( np.mean( [ np.random.exponential( 0.5 ) > 5 for i in range(N) ] ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does this all have to do with Bayesian statistics? \n",
    "\n",
    "\n",
    "*Point estimates*, to be introduced in the next chapter, in Bayesian inference are computed using expected values. In more analytical Bayesian inference, we would have been required to evaluate complicated expected values represented as multi-dimensional integrals. No longer. If we can sample from the posterior distribution directly, we simply need to evaluate averages. Much easier. If accuracy is a priority, plots like the ones above show how fast you are converging. And if further accuracy is  desired, just take more samples from the posterior. \n",
    "\n",
    "When is enough enough? When can you stop drawing samples from the posterior? That is the practitioners decision, and also dependent on the variance of the samples (recall from above a high variance means the average will converge slower). \n",
    "\n",
    "We also should understand when the Law of Large Numbers fails. As the name implies, and comparing the graphs above for small $N$, the Law is only true for large sample sizes. Without this, the asymptotic result is not reliable. Knowing in what situations the Law fails can give us *confidence in how unconfident we should be*. The next section deals with this issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Disorder of Small Numbers\n",
    "\n",
    "The Law of Large Numbers is only valid as $N$ gets *infinitely* large: never truly attainable.  While the law is a powerful tool, it is foolhardy to apply it liberally. Our next example illustrates this.\n",
    "\n",
    "\n",
    "##### Example: Aggregated geographic data\n",
    "\n",
    "\n",
    "Often data comes in aggregated form. For instance, data may be grouped by state, county, or city level. Of course, the population numbers vary per geographic area. If the data is an average of some characteristic of each the geographic areas, we must be conscious of the Law of Large Numbers and how it can *fail* for areas with small populations.\n",
    "\n",
    "We will observe this on a toy dataset. Suppose there are five thousand counties in our dataset. Furthermore,  population number in each state are uniformly distributed between 100 and 1500. The way the population numbers are generated is irrelevant to the discussion, so we do not justify this. We are interested in measuring the average height of individuals per county. Unbeknownst to us, height does **not** vary across county, and each individual, regardless of the county he or she is currently living in, has the same distribution of what their height may be:\n",
    "\n",
    "$$ \\text{height} \\sim \\text{Normal}(150, 15 ) $$\n",
    "\n",
    "We aggregate the individuals at the county level, so we only have data for the *average in the county*. What might our dataset look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 4) \n",
    "std_height = 15\n",
    "mean_height = 150\n",
    "\n",
    "n_counties = 5000\n",
    "pop_generator = np.random.randint\n",
    "norm = np.random.normal\n",
    "\n",
    "#generate some artificial population numbers\n",
    "population = pop_generator(100, 1500, n_counties )\n",
    "\n",
    "average_across_county = np.zeros( n_counties )\n",
    "for i in range( n_counties ):\n",
    "    #generate some individuals and take the mean\n",
    "    average_across_county[i] = norm(mean_height, 1./std_height,\n",
    "                                        population[i] ).mean()\n",
    "    \n",
    "#located the counties with the apparently most extreme average heights.\n",
    "i_min = np.argmin( average_across_county )\n",
    "i_max = np.argmax( average_across_county )\n",
    "\n",
    "#plot population size vs. recorded average\n",
    "plt.scatter( population, average_across_county, alpha = 0.5, c=\"#7A68A6\")\n",
    "plt.scatter( [ population[i_min], population[i_max] ], \n",
    "           [average_across_county[i_min], average_across_county[i_max] ],\n",
    "           s = 60, marker = \"o\", facecolors = \"none\",\n",
    "           edgecolors = \"#A60628\", linewidths = 1.5, \n",
    "            label=\"extreme heights\")\n",
    "\n",
    "plt.xlim( 100, 1500 )\n",
    "plt.title( \"Average height vs. County Population\")\n",
    "plt.xlabel(\"County Population\")\n",
    "plt.ylabel(\"Average height in county\")\n",
    "plt.plot( [100, 1500], [150, 150], color = \"k\", label = \"true expected \\\n",
    "height\", ls=\"--\" )\n",
    "plt.legend(scatterpoints = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we observe? *Without accounting for population sizes* we run the risk of making an enormous inference error: if we ignored population size, we would say that the county with the shortest and tallest individuals have been correctly circled. But this inference is wrong for the following reason. These two counties do *not* necessarily have the most extreme heights. The error results from the calculated average of smaller populations not being a good reflection of the true expected value of the population (which in truth should be $\\mu =150$). The sample size/population size/$N$, whatever you wish to call it,  is simply too small to invoke the Law of Large Numbers effectively. \n",
    "\n",
    "We provide more damning evidence against this inference. Recall the population numbers were uniformly distributed over 100 to 1500. Our intuition should tell us that the counties with the most extreme population heights should also be uniformly spread over 100 to 1500, and certainly independent of the county's population. Not so. Below are the population sizes of the counties with the most extreme heights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population sizes of 10 'shortest' counties: \n",
      "[112 112 292 175 360 166 540 135 101 113] \n",
      "\n",
      "Population sizes of 10 'tallest' counties: \n",
      "[122 148 141 141 134 237 117 281 158 216]\n"
     ]
    }
   ],
   "source": [
    "print(\"Population sizes of 10 'shortest' counties: \")\n",
    "print(population[ np.argsort( average_across_county )[:10] ], '\\n')\n",
    "print(\"Population sizes of 10 'tallest' counties: \")\n",
    "print(population[ np.argsort( -average_across_county )[:10] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not at all uniform over 100 to 1500. This is an absolute failure of the Law of Large Numbers. \n",
    "\n",
    "##### Example:  Kaggle's *U.S. Census Return Rate Challenge*\n",
    "\n",
    "Below is data from the 2010 US census, which partitions populations beyond counties to the level of block groups (which are aggregates of city blocks or equivalents). The dataset is from a Kaggle machine learning competition some colleagues and I participated in. The objective was to predict the census letter mail-back rate of a group block, measured between 0 and 100, using census variables (median income, number of females in the block-group, number of trailer parks, average number of children etc.). Below we plot the census mail-back rate versus block group population:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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//u9zyy23NNOr4PDv23XXXcezzz7LZz7zGXbv3s0XvvAF/u3f/m3RY2/ZsoX//M//5L777mPnzp3cfvvtPPLIIzO2Oeecc3j22Wd55plnGBsbo1arzdnPxo0becMb3sA73/lOfvCDH/Dss89y22238Ytf/II//uM/Pupro9FoTjLLmbCv0WhOT0ZGRtS73/1utXnzZuW6rlq1apW6+uqr1Ve+8hUVhmFzu3/8x39Ul1xyiXJdV7W1takrrrhCfe5zn2s+P1/x5/TCvYMHD6rXve51au3atcpxHLV69Wr1jne8Y0ZB4Y4dO9TVV1+tMpmM2rRpk/rmN785owD2/vvvV7/0S7+kurq6lOu6atOmTeov//IvlZRywfO75ppr1K233jrjsWuvvVa97W1vm/HYjTfeqG655Zbm39/4xjfUpk2blOu66tJLL1X/+Z//qUzTVP/0T//U3IajLIC99tpr1Sc/+UnV29urUqmUeu1rX6tGRkaa22zdulW9+MUvVqlUSm3YsEF99rOfnTPmIAjU7bffrjZs2KBs21Zr165Vt91224Jje/jhh1Vra6t63/vet+DYZr+mwe233666u7tVJpNRN910k/ra176mAPXcc881t7nttttUd3e3EkLMGOfh7puFuPvuuxUwp1B5KffRbJZSdPqd73xHveAFL1CO46iuri7127/926pcLjefX8r7ppRSH/3oR9WaNWtUNptVb3rTm5pOPwuNZWpqSr3hDW9Q+XxedXR0qHe+853N97XB+Pi4uummm1RLS4sCmvfg7PutUCio3/zN31RdXV3KcRz1whe+UP3gBz9oPr+UInONRrO8CKWOYA1Yo9FoNBrNknj729/OwMAA99xzz3IPRaPRrGB0mo1Go9FoNBqNRnOaosW8RqPRaDQajUZzmqLTbDQajUaj0Wg0mtMUHZnXaDQajUaj0WhOU7SY12g0Go1Go9FoTlOsw29y+jC78UZXV9eS2rZrjg/6ep989DU/uejrfXLR1/vko6/5yUVf75PP6XrN16xZs+BzOjKv0Wg0Go1Go9Gcpmgxr9FoNBqNRqPRnKZoMa/RaDQajUaj0ZymrKiceY1Go9FoNJpTEaUUvu8jpUQIAcDw8DC1Wm2ZR3ZmcSpfc6UUhmGQSqWa98hS0GJeo9FoNBqN5gTj+z62bWNZh6SXZVmYprmMozrzONWveRRF+L5POp1e8mt0mo1Go9FoNBrNCUZKOUPIazTzYVkWUsojeo0W8xqNRqPRaDQnmCNJm9Cc2RzpvaLFvEaj0Wg0Go1Gc5qixbxGo9FoNBrNKURp6w52vO+veeL172Lr2/+EoW/+AFkLlntYc+jv7+db3/rWcdnXL37xC3784x8fl32dLJ566in+9E//dNFtCoUCd9555wkdx4oW83EtIBidQAbhcg9Fo9FoNBqN5rDs++y/8Ph/fycj/3Efhuvg7zvIjvd8gide/y7CqeJyD28Gx1PMP/PMM9x7773zPhdF0XE5xvHmkksu4SMf+cii2xSLRb785S+f0HGsyEoM/+AI+/7mnxn99n8Sez5mNk33a6/j7NveirOqY7mHp9FoNBqNRjOHiQd+zt6//hLd//1aNn/kNqx8FqUUY99/gO1/8DH6PvC/uPCzf3ZU++7v7+eWW27hiiuu4PHHH+fCCy/kjW98I3fccQdjY2N85jOf4bLLLmNycpI/+qM/Yv/+/aRSKT7xiU9w4YUX8tOf/pQ/+7Pk2EII7rrrLj72sY+xa9curr/+et7whjfwm7/5mzOO+b//9//m29/+NkEQ8KpXvYr3vOc9fO973+POO+/kX//1XxkZGeFXfuVX+PrXv85f//Vf4/s+P/vZz/i93/s9du3axfDwMP39/XR0dPDhD3+YP/mTP+HAgQMA/Pmf/zmXX345d9xxB/v372dkZIQ9e/bwwQ9+kMcff5z77ruP3t5e7rzzTmzbZuvWrfz5n/851WqV9vZ2Pv3pT9PT0zNjvH/wB3+A67rs3LmT0dFRPvjBD3L99dfj+z7/83/+T7Zu3Yppmnzwgx/kpS99KQ899BCf//zn+fKXv8wdd9zBgQMH2L9/PwcOHOAd73gHt956Kx/72MfYt28f119/PVdffTW/+Zu/ye/8zu9QKpWI45i//Mu/5Morrzyq97TBSRHzn/vc53j88cdpbW3ljjvuAKBcLvPpT3+a0dFRVq1axR/+4R+Sy+UA+Na3vsW9996LYRj8+q//OpdeeumSj+UPDPPE63+fqFhhw5tejXnOWkpbdzD0je8z+eBjXPaNv9GCXqPRaDQazSnHgTu/hd3VzpZP/DGGYwOJcF5109WUtu6g/x/+jdrgKO7qVUe1/7179/L3f//3fOITn+C//bf/xt13383dd9/ND3/4Q/7u7/6OL33pS9xxxx08//nP50tf+hIPPvggt912Gz/60Y/4/Oc/z8c+9jEuv/xyKpUKruvy/ve/vylmZ3P//ffz3HPP8Z3vfAelFG9/+9t5+OGHuemmm/jud7/LnXfeyX333cd73vMe1q5dy3ve8x62bt3KX/zFXwBwxx13sHXrVr71rW+RTqf53d/9XX7jN36DK664ggMHDvDmN7+Z+++/H4B9+/bxjW98g507d/Ka17yGL3zhC9x+++3ceuut/PjHP+baa6/l9ttv55/+6Z/o6enhm9/8Jn/1V3/Fpz71qTnjHhgY4Jvf/CZ79+7lDW94Ay9/+cubaTI//vGP2bVrF7/6q7/KAw88MOe1u3bt4hvf+AaVSoWXv/zlvPWtb+X9738/O3bs4Ec/+hEAn//857nmmmu47bbbiOMYz/OO6r2czkkR8694xSt41atexWc/+9nmY3fffTcXXXQRr33ta5s301ve8hYGBgZ46KGH+NSnPsXk5CQf+chH+Ju/+RsMY2kZQc998h8JCmX6XvV2tooe2Atm15Vc8t7LqP3VHez726+w+SO3zXiN74Xs6xvH9yJSaYuedS0MDxSbf2/Y3Alw2G1SaXvOvhqPz4fvhfQ9M8xQfxEU9G5oZfOF3Qtuv1SOZAzH8zjZK/PHdX8natynKmf6+Ws0Gs2ZTvnpnXS+8sqmkJ9O1w0vo//z/0rpmb6jFvPr16/nggsuAOC8887jZS97GUIIzj//fPr7+wH42c9+xhe+8AUAXvaylzE5OUmxWOTyyy/nz//8z3nd617HTTfdxJo1axY91v3338/999/PDTfcAEC1WuW5557jqquu4iMf+QjXXnstL3jBC3jta1+74D5uuOGGpt/6Aw88wM6dO5vPlctlyuUyAL/0S7+EbdtccMEFSCn5pV/6JYDmee3evZsdO3bwpje9CSEEcRzT3d097zFvvvlmDMPg3HPPZcOGDezatYtHH32UX//1Xwdg06ZNrFu3jj179sx57bXXXovruriuS1dXF6Ojo3O2ufTSS/mjP/ojoijixhtv5PnPf/6i13EpnBQxf+GFFzIyMjLjsUcffZQPfehDAFxzzTV86EMf4i1veQuPPvooL3nJS7Btm+7ubnp7e9m1axfnnXfeYY8TlSqMfOd+xra8kFrHoaWTOIDHDzpccMllDN31Izb+2e9i2Mmp+17IEw/1IwyBYQhKBZ+nHz1Iz/oWXNfCq4YMH9yLEGA7FoYhKNa36V3fglPfZmK0nwsu62X7E0PNfTUev+wl6+eIMt8L+dn9exkZLGHULYgKTw0zNVbl8qvPPmoRN/t8FhvDsTDfcf7r3j7Ov/ToBOjJGvepypl+/hqNRqMB4dhEhfK8z4WFJF/ecJ2j3r/rus3/NwwDx3Ga/x/HMZB0IZ0zLiH4vd/7Pa699lruvfdebr75Zr7+9a8veiylFL/3e7/Hr/3ar815bmhoCCEEo6OjSCkXDNhmMpnm/0sp+fd///d5myk1zsswDCzLalo7Ns5LKcV5553Ht7/9bSzLWjQHf7YtpBBi3msyH9Ovr2mazWs6nauuuopvfvOb/PjHP+a2227jt3/7t3nDG96wpP0vxLIVwBYKBdrb2wFob2+nWExu0omJCTo7O5vbdXR0MDExsaR9huNTEMf4XfPPFqeyq5BVj7hUaT62r2+8KaAAilM+woDSlA/QFPjFKb+5Tam+TXHaNsIQPPnT/hn7ajy+r298zlj29Y1TKvgYQiDq/wxDUJzy591+qcw+n8XGcCzMdxzjGI5zssZ9qnKmn79Go9FooOv6lzB+78P4A0MzHldKcfAr/w+rNU/r5Red0DFcddVV3HXXXQA89NBDdHR0kM/n2bt3LxdccAG/+7u/yyWXXMKuXbvI5XJUKpV59/OKV7yCr3/9683nBwcHGRsbI4oi3v3ud/PZz36WzZs38w//8A8A5HK5ZqR9Pq655poZrjC/+MUvlnxOGzduZGJigp///OcAhGHIjh075t32P/7jP5BSsnfvXvbt28fGjRu58sorm4W+u3fv5sCBA2zcuHFJx85mszPOa2BggK6uLm655Rbe9KY38fTTTy/5PBbilCuAXersB+Cee+7hnnvuAeDjH/84vZvPRQmD1PgQbLp4zvbOyCDCdek5+6zmEtYuY5Jc7tCcxhAerutiCLM5IzQNH1DNvw3h47oCQxgzZo3lQkgul51zXNOw6erqmvHYLmMS07BxHGPWtgam4c7ZfqnMPp/FxnAszHccwzAwDfOojnOyxn2qcrTnb1nWGXF9ThX09T656Ot98tHX/MQxPDw8bwfY6Y9t+I3/wfBdP+KpW97Dpj/5TTpefjn+gSH2fe5rTNz7CJve/1u48+iMpWCa5ozjGYaBaZpYljXjufe+973cdtttXHfddaTTaf7u7/4Oy7L44he/yH/9139hmibnnXce119/fTMSfv311/M//sf/4Ld/+7ebx7v22mvZvXs3r3nNa4BE1H7uc5/js5/9LFdddRUvfelLueSSS7jxxhu54YYbuPrqq/nc5z7HDTfcwLve9S4Mw2juH+BjH/sYf/Inf8J1111HHMdcddVVfPKTn5yz3exzNIxEq33xi1/kAx/4AMVikTiO+Y3f+A2e97znzbhGhmGwadMmXv/61zM6OsonP/lJcrkct956K+9973u59tprsSyLv/3bvyWbzWKaJkIILMuadxymadLd3c0VV1zBK1/5Sq699lrOP/98PvvZz2LbNtlstnl9p9NI01kqQh2Jej4GRkZG+Ku/+qtmAextt93Ghz70Idrb25mcnORDH/oQf/M3f9Oc+bzuda8D4C/+4i94wxvesKQ0m4MHD/Lvr/x9cgO72X3zrQRthy5EamyQc7/zT2SvfTlX/P3tzcd3bB1ictxrRkTHhst4lYB01qGrJynIHR0qAbCqN7/gNlIq/GpAKuM099V4vL0zzZaLe2eMdcfWIXZtH6XmRc0lHaUUbtpi0wWr5my/VGafz2JjOBbmO04qlcZNq6M6zska96nK0Z5/V1cXY2NjJ2OIGvT1Ptno633y0df8xFGtVmcEAIF5Uz6KTz3L9nd9FH//YPMx4dic9Tu/yobb3qo7yR4ji6XZ/MEf/AHXXXcdv/zLv3ySRzWT+e6VxWoUli3N5kUvelGzCvn+++/n8ssvbz7+0EMPEYYhIyMjDA4OsmnTpiXvt+d3b0GZJhu//UV6H/kBbTufZPVD3+Xc796JyOc5/3/eOmP7DZs7UVIhZTKnaWlLoSTk21JAIqjyrSla2lLNbfL1bVqmbaOk4tIXr5+xr8bjjQLa2cfNt6aQSqHq/6RUtLSl2LC5E98L2bF1iKceGWDH1iF8b2le+bPPZ7ExHAvzHUcew3FO1rhPVc7089doNBpNQssl53PFvf/MRXd+nI23/w7n/dV7ePHDX+fsP3ibFvKaeTkpkfn/9b/+F9u2baNUKtHa2sob3/hGLr/8cj796U8zNjZGV1cX7373u5vWlHfddRf33XcfhmHw9re/ncsuu2xJxzl48CAAj3zzYYY++zVa925HKIk0TayrruDij7yT1nPmzmyWw83G90L6to0wsGeSSqlGNuewbmMHmy9MqqunF0M2hN1SiyGXy83mBVdupuKVjtv+zjQ3l6M5fx1FO7no631y0df75KOv+YljqZF5zYnldLjmRxqZP2lpNieDhphv0OqkGNr9HE5HG2b2UPXzkYqm6dsblgClkDFHLThnO5fMFuvzpVzUahFxGNPelZ33uKeCENY/Aicffc1PLvp6n1z09T756Gt+4qhUKmSzM/PdTwdhudI4Ha75fPfKYmL+lCuAPZ7YLTnS61fPeOxILQCnby9jycH9BRCwen3rUdsHzudcIuuPb7m4N5k0TBPyYRgzcrCEZRlkcu6c42pbQ41Go9FoTm0MwyCKonmLYDWaBlEULbm3UoMz7o46nJBebPuJ0UPR8sKER1dPbtHXLsRssR6FMVMTHhMjiX2TYSXR+unHQilsx5x3zAudU9+2ERzbPGPTVjQajUajOVVIpVL4vk+tVmvmvruuS61WW+aRnVmcytdcKYVhGKRSqSN63Rkn5mcLaUjEr+/Nv+QyffsolM0PYBTKBV87X8oLHMq5nxyrYNomrmsRhTGD/UVAkc46TI57BLVoRpOqMIgBQVvHoVSh6ced75xkLNm9bZTeda06Wq/RaDQazTIjhJjT8EinNZ18VuI1P+PEfCqddGydbQGYSs+8FL4X0vfMMH3PDBPUYvKtKRCq6YNv2ca8r52aqPLgD3cTRxLbMWlpSzEyWEIpcNxEnFu2yVB/kZ71LfXmVAqEoLUjjWEIHNcim7dxHAvfi2htT2HZJpZtzjvm+c5pcryK7ZhLXoHQaDQajUaj0Zx+nHFifsPmTiZG+5Ewo/h0ugWg74X87P69iQiv/10u1XCcpDlAKmvT09Ey57W+F/LgD3ZR85N8J78a4lVCbNfANI2mT73jWvSubyEKY1CQzjq01qPuY8NlolCSzli8/FWbZ+TEVysBIweKBLUY2zE4Z8t5C55TFMR0rU6O10jjiUJJadJbcrpNw3FnaF9SJ9C7voXNz+vRkf15OBUKkDUajUaj0Zx5nHFiPpW2uewl6xcVXvv6xikVfAwhkAqEIRBAHCkyeYt02sJ1TfKtqaaQf/rnA+x4aphCwcdxLNJZG9MwAEVpqoaop8WUi8l+3ZTF+k0drF7fyuS4RxxLhgaKNGLrQSB44qFDaTHnXtDJD7/5LEpJTNNAGIIHvreb6153Pm0dmTnnlM3bVEphM42nYU0bWDP3O5uGKC0VahzcP4VXDbHrKwKFqREmxzyuuOZsLVSnoQuQNRqNRqPRLBdnnJiHRNAvlmriexFxpBBCUPMCBALHtespMDZeJWT39jF61uZJ52yeeKifwoSP7wfEsaLmRXjVkNaOFKZpUCl5GIZJYbwKgDASh5qdW4e54NJehgYK1Bq2lwhq1ZBYSkaHSjzXN8aa9a34tZBMzp5R4Syl5Mmf9vOKV2+Zc04NgTk5Xm0KeQW0dWaIYsnD9+6ZY3M5XZROjFYoTHrEkYIcRIEkjiSDYYG+Z4a56EXrTsRbc1pypEXVGo1Go9FoNMeLM1LMH45U2sK0BFGokJJ60atCKsXESAXbMTFNg6EDRZ59agipoOnWr+pivRYzPlxBSTAMiFQMyW6QEfhxhGkJtj0xSL7NTYSzlNi2RTZnMzFSRUqJV4kIaxE1P6atMz1DzBuGQbU8f2fYxgrEA9/vq09MQKEYOVCkUg7I5pw5NpfTRWkUSlACpSTFSR/HMQFBzY/Ys33slEu3Wc40lyMtqtZoNBqNRqM5XmgxX2d2Y6h0xsarhokIVhIQxFF8qPBVKabGPOJ47r6UnPnfhohv/k0i/qVUVEpJXr1hCpSEsBYxFcTI+NDswKuEKAWlKZ+OVbnmPqSUZHLzC9bG+aCS43teiGUaeJWAMIiplAO8akCllPz98L17yLWkmqLUsg2EAXEk6zMVgVIK0zSwHHOaJ/6h69bVXSHbKubtjHsima/oeGK0etLSXJZaVK3RaDQajUZzvNFqg/k7stqOyblbOhg+UGJyzCPX6lKY8IkjiUJR80JiucTmudMj93VhDyDrEwEpqfvKJ9upWfuNYzBMCGoxpaJPGMQoqXBck5fduHHOufQ9M8ye7WNYTpLXP3SgSBRL8i0pwkgSRRJQ9D0zguNaCASVUo1ci09Le5pSwac46eFXQ4IgxnLMpouPk7Jo78zge1HzusWxpDjl079nismxMuvObSc7T3OrE8FCRcc9a/MnLc1lKUXVGo1Go9FoNCeCM17M+17Iw/fuoTDpYzsmbR1pLNvEdiyyuRQ3veHsZvT56Z8fxKuGoBSRMBDELEXOy2kR+cVeMGO72c/FgAHlYq0Z4a/5Mf/53Z1s3NJNvtWlrSvNo/fvozjlE8cSJRXjwxVQChlJChMeUSyxLAhDRRRIwiBOou22gUIxMVqtNy0QGKZIViZkjOPY5NpSdHXnMEyDVNpiX984cSwZPlBCSkmlFBLUQvY8O8amC1eRzjgL5o4fr7SYZAyqmX6klMKrhvTvmcQrBzP2e6JScZZSVK3RaDQajUZzIjijxPxsMdezroXtTwxRmEyi3aUpn7GhMu1dGTq6s3Nyntee3cauZ0aIY1nPQT9CpkXljwo59+Vjg1X8ygFyrSnGhioIkQjbWi3GMJJ8/UYqkGkm7jqBr5CKZuqPkhJhCCqlACHAQBBECgQ4roFSAoTAskyCIKZcrGJZgsmxKp4XIqWkXKpR8yKUgjiKeG7nOB2rMqAEpSlvjqg+Xu4vvhdhOyZ+NUQpRalQQwiQsSII46ZzD3BCHWcOV1S9UtGWnBqNRqPRLC9njJifT0D2bRultSONqEe8AYSAwqRHrRax5aLuGa8zTYP157azd9cEtmMiBMQyJlxiV+Ak//74n1u5GFIph9Ny8hP1LuNDqTwAkZTzTiiEIVBSEYUSpcB2jGQ7Ab4nMc0k9cf3QsaHK6w/tw0pIQhiJkaqRGFEEBxaVhBAtRRgmIJcziUIBD+7fy/tqzLISB3qgGslt59hCIIwntdh53Ck0hb5thReNZxW46AwLYO2zgzCEEntQP08tePM8UNbcmo0Go1Gs/wYh99kZTCffWAcSUpTPgKBQiGlTLzgCzUKEx5eJZzzOtu16FiVxXZMVq1pobu3BWOJV1GIw29ztKhFUnRmbjj3bxknnW0bE41E1CtkrJoTEMsxSaVtMjmbaiVx0Mm3pghqEbWabPrjK5mkCykFXjmkXKwR1iKGBors3zVBzY8oTPqMHCwRhnH9eDHDB0oUJn1qfsTkuMcTD/Xje/M79Uxnw+ZOLNOge00ewzQQQmBaJuvPbce2zaarjHacOf7M95maPnnSaDQajUZz4jmDIvNzxZztmIRBjBCJe83kmAdKIQyBjCVPPTLA6vUttK/KEccwcrDI+EgVUKTSNjKOKZdqWK4g8A4fcpfzON+cKkThofEnqTLT/hZQLvrUvBDTMkHA0IECE8MVRJK5k6TtiNn7lKSzNuViQBjFmJbB2HCZajkgimLGhsrYjpnk8kcxbR2ZZrfaMIh58Ee76FmTR8YsGK2fka9eDgjCmLbOTLPR1XRXGe04c3zREySNRqPRaJafM0bJzGcf2NKWYmq82swXT55LnpdSYZiC8ZEK1UpELCXlQg0ZS4QQeJWAmhcRhtGihasrAgV+NSY0Y6SCqXGazjuGKWZsNzuNZ3y0gmkKTDNpmiXIYLsmvhcyuH+KbEuqGYEvFjx8L8Q0DZRSHHhuiqlxj9XrWxdN4Wjkq2/Y3NlM+wCo1SIK41Usy8DNWAS1CMe1mo4zYRARBBFPPTKg872PAm3JqdFoNBrN8nPG/OrOZx9omgYvu3ET/XsmGOwvNoVpQ7BblolhmtS8xKJRSVVvIJXg+9GxFbSeRgjBDE/9xgRmegQfmHM94lChYkUgJbZj4FXDZEUkjIglVMs1LDupP6iWQjwjwHFsvGqAUlCrhZSnfFq7MjiOuWj32elR+lLBpzDh0daZSfz8i0lxbzZvI2MwLMHkaERhwqc4lRRA920b5WU3bKStI3PU1+lULQg9EeNaLkvOU/UaazQajUazHJwxYn4x+8C2jgzDA0UO9heTBk0SRP3KOK6J61qMj1WTzqiRTAo2ays9HD8TYYA60jShxASHhm2+lIqgFlEq+iipksJalTSmklIS17Mzat6hiuI4grAWEgQlUlmbmhcu2n02lbbZsLmTh+/dQxwl/vetHWnsut2o41hsubiXHVuHMAzB8IESQiRdfqMw5P7v7ODcC7uRkVpQKC4kJhsFoVGc1GKEQUzfMyO87MZNxzRBOFZOVKHqclhy6qJbjUaj0WhmcsaIeVjcPvCiK9bSv2cKYSTCTklFWItZtSmHVw1Jpy18L0TEgjA4s4Q8HGW+fz0Np1FsjFIEfpw8Uc+zNwQzhPxChIEkChP7y//83g66e3OMD1eJYoVtC9o6MrhpizCM6d81mUwYVPI+ToxWWH9uO+mM08zn9r2I4pTfFPKQTCwKkzX275pgVW9+XqG4mJjc1zdOFEtGDpYSMyAhiMKYB3+4m+v++/knRWzON9GYr1D1eDn5nGxLzhN5LhqNRqPRnI6cUWJ+PnwvpG/bCDueGsYwwUQgI4Vpm2TzSRfTlrYUgR9R8yOEOEPyao4TcaSQIvGsN02RCNxoWprOEeQpNaL4+/smGNxfJJN18CohcRzzXDxOGCY7dVIGUaCanXwtS7B/9yTrz2mjWq7x1CMDTI5VqHnhzLSpaohhimbq0HxCcTEx6XtR3R1p2gQBKE56PPD9Ps7a2HFCm1gtNNGwHXPFFKrqoluNRqPRaGZyRot53wt59Cd7GT5QoloJUEpgGiaZVotc3kUpcByTy68+m1/8/CCWU2B0sEwUhfXGRMt9BqcHqt6gCgRBEB99nYE45LTjuFApBQDUvHiGf3+tmoh6YdRtNqVAiJj+PVOcvaWTmh9h2iblYo1UxsayzMSKUyrSOQfLPuQ1OlsoNsRkGMYUJrxmPcXooEO+1aVaDqj5ETJWIBLvftsx8aqJ5WYjig/Hv4nVQhONctEnlXEOW6h6PCcXJ67b7vIV3fpeyFOP7mNsZFLn6ms0Go3mlOGMFvP7+sYpTvkY9YZQSiaiMA4lpmXQsSpLe2eaVNom3+oSRXmUUgz1F8/IVJtj5ZivWd0THyCKVNMLf6G5gRCi6e0fhZJsi0tpymcylFi2Qe/6FsZHqjiuwHYsLMck8CPaOtLJeMOYqfEqhmkwOVYh1+I2m4uNj1aRcdL5NgxiyoUaQRAxMVLBcgxM0yQIElGfzScThOlRfDj+TawWilrnWlzCQC5aqHp8u/KeuLz25Sy6feKhfrK5LDU/0rn6Go1GozllOKPFvO9FxFHiUNPIt240SQrr7jUNkbBhcycH9+9maszTQv4UoOZFh5pwLaTmVZIuZdsmKEnNizANo5nL7lVhw6Z2OrqyiRA2YWrcwzANwjBmsL+AjCVKwlgQoSTk21yKkz6ZXJJ/H/hRvVGVoDDhg6GIAklsSKQEyzYIajGt9QnC9Ej/8U4XWShq3dqebubOLxQpP5656Cc6R/9kF92CztXXaDQazanLGS3mU2kL0xJEocI0DHItbpI3bQha21Nc9pL1+F7Iw/fuoVSoMTZcIox0bu6pgjpMuo6UYDuJIb7vR5iRQsWSdM6tr8RIKqUaHV1ZABzH4ryLutn+xBDDB0pIJYkjiVdJJg6mZVApJpaZURhT80OUShx7olASxzFCGAgDUhmHajkgqEUz0nZOZBOrxaLWhytUPZ656Cc6r/1kF92CztXXaDQazanLGS3mN2zuZGSwxHClhGGAIQSpjE336jxXXHM2vhdyz7eeRSpJuRjge0l0VnN6kMlZhLUkdcpN2QS1CK+qCCNJvjUFQHGyxmSLh2EIigWfJx8ewElblMs1Qj8mlgrLSgp3ZRBjmALLNupFso00HkEcS6IQEBLTFPheiOUIoiBZBRgaKNK9Jo9lGs3VnsOlixxp3vmxRK2PZy76Smwm1Tin6Zzu56TRaDSalcEZ90s0WyBddPla2romGdpXAAG961uaPuYP37sHqSSVcoDvhVrIny4IcBwDy7aAiGw+RaVYqzvpSGJfYpoBrR0pbMdqis7JsQrVckBxyiOOFHEjPz9UWHbS2jaOFJmcTbng4zgGfiyBJH8fA6jfI0l6TiL8kyJbgziMufzlG5riejHhPT3vXMaSgb2TPPvUEOde0HVYn/1G1PpIJgPHMxf9aPZ1qjeCap5TvWnCycrV12g0Go3mcAilDpescPpw8ODBGX93dXUxNjbW/Ht2YV7jB3mhIrZvf20rI4NFan6shfxphDCSlBglJbZtNkV5GMikjwCQzjkYwIbzOrFsk8KEx4H9U4S1xKJovvfbtg2ctEkunyKVtVASxkcqyFghY0kUSYQAwzCIY4lSySQgm0+RyTpEQczZ53WSb00t2Iyq75lhhvqLTIxViWOFmzKplsMkJcw0sBwTQ0DvulbyrS4vuHIzFa80Z6xHeq83XtMQ1IYJCLFo86zFOBJxfjRjXQ58L2R8KJjXzeZUn4ycrsz+DtecePQ1P7no633yOV2v+Zo1axZ8bsVG5uezkTuSIrapiSrjI2X8apyoP81pg5IQ1YuU4zjGsgyiSDafU0AUxHT2ZClMVAnDRIzHoVx00hbHEoFJZ3eGrt5cUvAKlAs1vGpIxjVRCoIgborSKFB4lRC/GmE7Bvt2T9KxKkPfMyNNQd6zroX+5ybZ+fQw1XKA7ZoUJ31krDBNA9NKmm6lczblYo1s3mFksEQUSf7r3j7Ov3SucDyags1GVP94uNEcSV776VJcmkrbXHL5asbGsjMen5qo8uAPdhHHSV+DfFuKidHqKTcZ0Wg0Gs3KZEWK+YVs5BAwOVYlqlsTtnWksWxzThGb74U8+MPduBmbSik8el90zfKjkuLU2SSFz5JKMcJNW5QKtWYEfz6ESCL+SkL/nimq1YDihI/jWqTSNlEkiYKY1s40lVINvxqBAtM26qkZipZsCiklA89NkcnajAyW8L2Qpx89iJMy8MohcSSb96MQiZ99HAtMSxFPxbgpG8MwiEKJURfA84neYynYPNni+nQuLm18V9T8CMMw8KshXjWke03+lJuMaDQajWZlsiLF/HxiJAhjBp6brHfDNIjCmMFKSM/aPK3tqTmvjyNJyrXJ5CyqlUgL+pWESFJuquUaUipqXkQQLP4eG4m7JRIQSjHUX0IIQTpjY5gGrR0p/GqEXw3J5VOkMzGFCR/TMqj5EUopChMeAJZlNAV5ccpHGFAuNZxxDFAxAojiZBlBqSTtJIgkubZUPX0ncchJRG8473hHh0rEUbJta0ca0zSWVLB5tOL6aFNNTseC2ca57t81QXHSw3GTsYq6t21pyqelNXWYvWg0Jx+dEqbRrDxO3V/LY2A+MVKc8nFSFqoujoQQKCWZmvB40cs3zHm97ZhUSjUs28Iwo6Tbqxb0KwOViPm4nnqTythIf+HNhUhsLgHiSGI6FkoqTFtQq8Wcc147kDSZGtw/hWEK3LSLZZtMjFaQ9RfHkUwsLJ0kp95NW/UIuwE0ehyoZkTeNI2ksLZuf+nUm1qJVJJ2MzRQJJ0J6F2fmTFe3wuZGvfwKolAjsKYcrGGmzKxLMGOrUNzfsCn/8BPjlWwbLMpUOHw4vpYUnOWqxHU0TL9XD0vQkpFuVgj15JYngohCIN42SYjWqxpFuJENnTTaDTLh3H4TU4/Ummr6TrRIPlxteld10IqY2NaBumsQ2d3ln194zz1yAA7tg7he0mxYSZrUy4FxFGMZZmHGhRpVgTCqKfNqKQB1WIolZRNCJKUHakkxjTR1sA0DbZc3EP36jwdq7IYZiK6hUh+OJVSCAOklPheWE/zMoiiRMhHUUwYxAhDIRAoVJLGk0mKbWMlqfkRcSQpTHpMTVQZHSwwNlyZEZ3f1zeO7VisOauVVMbGMAQ1L0QYBlLC5LjHEw/1N1/T+IGfHPeo+RGmbTLUXySoJddlKeJ6vtUwUU8BOhwNS832zjSGAX41wHbMuiCdu+qw3Ew/V8s2SKVtlFL4detKKRN70uWYjMx+L2e/15ozm2P5nGo0mlOXFSnmN2zuREk1w0bOtAzybSls26S1LqJq9QjWyFB5xg9fz7qWJNKWd7CdJLrmOCZiRV6tMxOlII7q/10kV76BlMk/pRSBl/jN12p11xcOCd7Nz+tpClMUiS+9ZWCaJlY9zcWyLLI5B8s2yeQcyoUapinItaawXYOwpsBQiHpzqiCIsV0Dx7GQStWtMyWmaZDJuYwPV+jbNtIca2NlyrJNunpyOCmLbN5trizN/gHf1zdOHEsmRisc3DfF4L4pEIrRoRKGIWjvTB82cnesee+ptM2GzZ2EgSSVcQhqEbu2j/KD//sMT/984JQSo9PPta0jjRCCfGsKwzLqqzI2L7tx0zFHOn0vZMfWoRmBhsOhxZpmMU7n+hSNRrMwKzLNphHpS2zkQgxL4Lh5+ndPIQyo+RLDSDpwOimLkYMlete1YNsmEhgeKNK7rpWRwRJuSmKYSQQ2mBaF1ZzmHGXKlJKAqUApLMsknbYxDGhtT89IZ9iwuZO+baM4roWMVbNbbTbv4KZMWtrSuCmLarnG2Vs68SphMjkwTGw3sdSs+XE93aaxLiAQCoRpJBOBrINlmUgZM7SvwEUvXAvMzUFvFABP70RrGIJSwWfH1iF2/mKEwoSH45pUK3XBKKClLUUYxEtK0zgeee8NISpjyWB/sbkatn/3JOVCcNJTAeZLV4GZ52rZJqvXtzA5XqW1PcVZGzuOS1rL0aZDaLGmWYzTsT5Fo9EcnhUba05s5Daw5eIeJkerDPaXkMDkmEe5lOTP5/IutmUioFmc2Pjhy7e6dKzK0tWTxTSTglmdaXPmYpoCq66hpFSksw4bL+hi9Vnt5FtTbLm4d4bI2tc3TltHup6CQV2YKnwvor0ry1WvPJdLrlxHe1eWbM6lqyeH61oYBvW0LgPHNet59Mm/XIuLYRjJqtPs1YRpN+fslSnTEkilaO1IN7ep1SKGDpSYHPeoVgLCMKY45Sd2mpEk8CNKUz5xLJcU1Z1vNexI894bQnRqwkvcg0TSeTeO1EmPLi+UruJVanPO1TANOrqyvPxVm+fcB0fL0UbY50sx1GJN0+B4fE41Gs2px4r/hu97ZpiRwRJGXRgIkUQ3AZyUhV8Nk86g9ehl44evUZQ3OV7FspJIaBRKZKC7R52JxLHCMgS2I0ilLc45r4sojJkYrTAxUgGY1UQownEt1m5oY3y0QrlQA6CtK8PlV5/d3K4RKYvjpBi7kXftppN7zrZNlFLYtolpGti2QRRFBGFEuQiWHWFYSefiBo2VqUZU+axNHUyOVjHNZO4upaIwXqWtM4NhCBzHxBOCOJaEYbJqlRSIK4YPlJboSGMv2tF2KTSuRRQmdQZA07nnZEeXF7Ln3LltiLXnZI/5XA/HYhH2xQpcT7diYs3J5Xh8TjUazanHihfzQ/3FppCHJMIqpaRcqLFhUweDlRClJK5tzfjha3zpPfD9vrq9n0lHt8P4cJko1LY2ZyJRqDBthe1aibVpfxFIovST4x7DB/fSviqDjNQMR5jOVcnqThjEpGdFSDds7mT44F4GBwp4lSApqBVJtDfX4uJVkzSZoBZRKYGwBWaYiHyoF+cqWH9ux4z9zm7aNKO7qyWYHDOYGK1i2UlX2Ww+yVNHSUzTxDAFpmVSKftsf3KQ0aEitmPS1pEl3+rOKwCOpFHUbHwvJAgihgcKVKsBUb2Bl2EKOnuyJz26vJCY9ioBkG3m+Deu6b6+8eMqihZKhzBMFk2/OdFiTTvlnP4cy+dUo9GcmqxYMe97IY8+uIuD+wtEocRJWaSzNm7aJgxlPeJn0rM2z8RoGSEUpUmP3g2tM/ZjO0kesYCmL7lm5dNIb5mNisGr+Ox5dgyvGiBjhZs2GRyYIqwl3WbbV2WxbQO/GtHZk2Vwf5FYJgWrLe0pnngoEV+QRICnxstMjVWbx5UqcV+qVmoYhkE6Y2A7FlJKolpMa3sKKZOi7HxrllTGYHigSFtHZu6A68zu7iqlolYLKU5FRJHENAS2Y0A9Ih+FiqDmI4SB7QiGD5RRSjE55rF6fWuzw2njHEoFv27PmFpQ7C/E9Pzwtq4MUzuT1JZU1sZNWYwOlulZmz+p0eWFxHQ668wZ84mw+Fsowo4Qh23odaLEmrY11Gg0mlOTFSnmfS/kZ/fvZXLUR5iCyIuJyonHd67VJZ216ezJ4qYs0jkbKZOc3NKUz57tY+zbOc7l12xgz/ZxLNtMoqJln7Amm37jmpXNfELeMCDX7uKVQryK33SyqZQOpX/EkWR8qEJrZyK49++ewDSTJlEylvTvnqStM8W2Jw5Q8yRRLBkZbAh5geWaST68UkSBpHttnq7uHAoYGihSLvoENUk6YyMVdK7KEUbBklNQGukjqbTFgf0FkCoRhrYgjhXCUBjCSLzxZbKKpZRVP39BzY8oTvl0rMrSt22E8lQtOYeDJVCK8ZFqfYJcXbLIm57SUikF5FtTZHISKRVuysa0BG2d6ZMqGBcS0+dd2EvFK53wLrkLRdh3bB1etgLXoz1nHc3XaDSaE8uKFPONSKFhCDIZJ2nWE8TIWCKjmDUb2pp5yzu2DiEMwcjBUuIXIpImO/f8vx3kW1P1BkNRki+vhfwZSSNKLxVUiyEybtRXMMcVRxigUFRKIY5rIiXk21zKxRqCJAe8MOkzPnyQ1o40YRg3G0UBKKmwHRPLMrAdk961yUrR8IEiXiVJP4kCSSptIYRgfLRCrtXCqDeDOpxg8r2IuO4WY5mJGEuKXhPB7FUisjmHSinAqwZA4i6jVDI2o14caxiCoX0F8u1pSuM+SkpqfkwcSw7uSz5jSxW201NaGvnylmViWga965JaABnPfs2JFYgLiel01qXilU6Ka8x8EfbldCM5mnPW0XyNRqM58axIMe97EXGURBhN0yDfkqLmhSigY1V2RgGi7yWuHYLkR7HmBUSRTArxggjLthKXD+1KeUZi2hA37L3VIZvHxt+zURIiqZBxiBAK0xRJkTWQWM6oZmdWrxIkHvd1t5rGpEEIiGNJb28LUipkLBkfrSQTUqlQMulo3NKWwq8GVCtVwn1JU7S2zsyigimVthjYO4mMFaZpYprJiZiWQVBLVq5c18L3I0w76aoV1iTCiIGkSHZ8pJLYvdomhiEIahHlYlB37BH4fsTwgRKmZcw7wZgtxA2zng9eb8IUhcmHrWGlOVusHolAXKroX2i7hSYjyyWqj7XA9VgmQUdzzid6BUOjOVr0ipFmJbEixXwqbSV2fFHdms9Mur26aYuzNnbM+MCm0hZhXSSVizWUUvUum4pyKSSdQUfkz2Dio+xVJCVUyxGZvEXNizAtAyGSVR5hJI2F4jgR+6ZtQCiTwldDYJiCXN4l3+qye9sI1UpAzZd1u0awHYMoklQrAUoKnJRAIKh5EUMDRXrXtWCaxryCacPmTrY/NdTsTgvJBCKdsan5EY6TNJpq7UhzcF+ByfEKwgRUsr1pCQwzsXJtaUtRq0XU/IhkZmMAySQhjmP27xpvdr9tCO4LLutl+xNDM4R4GEQoBY5r0daR5mA5AAGtHel5xepiAnF6UaphwtS4h+1Yi4r+o4keL5drzLEUuB5rlPxozln73mtORfSKkWalsSLFfOIQUmJy1EepQ366LW2pOT88GzZ30vfMCMVqiFKKMIiTdAIjEWQ1/9TpPKk5/fCrEbZjEAYSx7UQhkG+zU0ETTXCSVmJHaSTWFFmcw6mJUhnbPr3TGHUI+aN6L0CwkBi2QKvEmJaFoVCDccySOeS/RYmPLp6cgvaGG68cBXP7RhjaszDMJNjQeJjn29NIWVihdm7voWaFyJMqFVDLNPEds1651mHzu4cU+NVbNvEQ6CUBCFwUiZ+NSKXd+YI7id/2k8qM/Nx27HItjg4tonvRWy5pAeUQsbMK1YXEogT41X6to0SRxLbMZt++WvOasUwzAWjwkcTPV5Oi7+jLXA91ij50ZyzblKkORXRK0aalcaK/EZNpW2uuOZsBvdV2LX9ICjo3dDK5gu757XTe9mNm/ju139B4CdL/cIxCYMYy04a9BhCoU1sNEdEPYpuWQaOazedkFIZi8CPsRyDfKubOCUJlVhRtrooqRAGTIx6mJYgDmQ9Oae+W5Gk8gS+AhRxKJGxwgsiwkjS0pYmCiW1WkRxqspzO8cSS8l6+s3I4F6yeRsVK9q60gghkpQby+BlN2wklbabYq21PUVbZ5pKMWDkYGlaJF/hpm0c16J3XSvlog9AGMY4KQvXtTBNA3fWZ80wBNVySCbnznlcRootL1zaj+h8AjGoRQw8N4ntmBiGgV8NKRdrZHIOU/XJTeNYs6PCRxs9Pt0s/o5HlPxIz3m+aH4YRARBxFOPDOj0Bs2yoFeMNCuNFSnmIfnRufxlmzjn/LbmY74Xzsnh9b2QJ3/aj2EIYilxnCTymNjz6fwazVFSj6QbpkEm6xCHEpQil3MpTFbxyjFWvQmUV5GYEkYHy0nthlI4jgnz9BxudEaNY9XMKTdNQS2IiWuSwqRHR1ea4f4iTjpxxvEqAZNjVdIZm2oloLUjQ9fqPFPjVcIgZuOFq2ZMdGf70z/xUD+mJZr9FRSHUmBa21M8/0VrZixZS6kYGyrR0paaMXYpFZm6e9RsIV4t15Ys7uYTiFMTXrLyUS8kbqT3+F6I4x76mpsvKnwk0WOvUltSofGpyHJEyWdH8w1LMDkaUSmFOr1Bs2zoFSPNSuOMuXOnJqo8+MPdzSX4lrYUB/dPMTpYxrSSxjkANT+mtcPG90OU1vKaY8EApZJiamGA7Vg4KQvbtojqdpJRGBGGEj+sN4uq/7ZUAollJaJ9+n2YpNokaWCGKfCqNeJYIuovlLHE9yJWr2+lOJXUgJSLAaAoTMYYhmBqrEpXd5ZVvXmkVDi2uaCQaoixvm0j7N422ozym6Yxp8Ha9PSL8y/pYfsTQ03h3sivvvTF65PHoVk8O9RfpGd9CzU/WpK4m368UqGWrHrEEt+vd49FYBhgu2a9XqF+bepj6FnXMkOQ96xrYWK0ethc8GTi34fve6elEF3OPP/GBHHH1iEc19LpDZplZbk7JU9Pf+zqrtDZ65wW3yGaUxeh1HyO2qcnBw8enPF3V1cXY2Nj+F7IPXdvp+ZHGEYSdVcKarWQOJKkMw41LyQMJV41wDDAciyUlASBRGknG81s6sJbwYKTPjctsCybOJQIS2BbBtm8S6UUEMWSsBYhDAj8QzsQxsL7M8zkh0ehiKNEzBsIokhiGOCkTLrXtCDjxN4SYGK0Uk+PEc3UMcsy6OjONlNP3JTFJVeuO+wpH6n7w0LbT398cqyCaZu4s6Ln7Z3pw4q76UVsI4NFxoYqBEGEW4/QKxStbSk2X9SDjFRTuE8vwG38iF9wWS/DA8VFz23H1iFqnsD3vSMe66nCcjt4PPXIQL1geiYL3YON73DNiadxb5iGSyxrp9Wq09GwXJ+F2cW3qVSaSrly2gQFVgKn6/fKmjVrFnzujIjM7+sbJ44VhpGkJSRL8Qq/GmGagnKx1nw8lbYJazG2ZSAwkHFEGOsQvWYmhjHX+xwSMW5ZBkpBFCqUikEpbGGSzjpJ+ocpiLwIBElx63SayfHJZMEwDYRQzbSaVNqhWqlhGnV/+EYxhwDbNulclWVqwqPmhViOiV9NCrhNy0hcaYBUxm6mkC22tDzfj910t5iGe8zh7CC3XNwzy0HqUKR2PnG31NzV6UVsAoEQSVfcMEhWIISAdee0c9EL1zZf0+grMTsyPDxQXMLkIcI0nKMa66nCcuf56/SGU5PpAjOXMyiXvdNq1eloWK7PwnzFt8IQenVKc0ycEd+gvhdh14XN9JxaISCKYkzTnPG4kzZxU3bibKNzbTTTqRe2LtR3QMn6NjSEOGRyLkEtBhSxlMRxvGATsuY6mUoi7a0d6cTqUUrCWtJgyrZNlFUX80HSKVUA2ZYUlm2SyTmMD1fI5GzcuniSQUxnT5Y4Vkil8KsBg/0FTMvg/Et65oxjPuu2kcG9TQvJhdJMjtTy7WjEXWOysHv7GAho60ijVOLGU5hIUmDSWQc3ZXFwfwHfC2f0lZhd+CZjyf5dE4eN0KXSFjVv5kKmFqJHxnKnN2jmR7u7nDx08a3mRGAs9wBOBqm0Rb4tlaRENK0qJW0dKUzz0CVQde+/szZ2YDuJgBLijLhEmiOg7sDIfLeGYUBYk2AkkXLHScRhx6o0QRBRLQVJMzNbzPv66fh+zNSER2HKw6uEQFKUHUfJPyEEqYydeMbHSUrN0IECxUmPdee2kc465FtTpDMO+fZUPZ8cSpMeRr3DbGtHmu1PDOF7My1Y5/txL075zc7KURgzMVphZLDEw/fuab6+b9sIE2MVRg6WGBsuI+v5/H3PDLNj6xBPPTJQz1dPtt+wuRMlFVIespBdTNw1JguT4x4I8CoBg/1FhICaH2HZyQpIJuvUbS9N9vWNN1+fSlvNYwFEYczB/QWCMKbmR0yOezzxUP+c69EYqzyCsWrm0qh3aO9M46Ys2jvTKzr6e7qgBebJY/Z3EOiggObYOSPuniQaVKV7TZ6psSqlgg8ozr+0lyiQPLdjjChU2I5B7/rWpHlNZ4bnnh2lWgqWe/iaU4n6d7BSh/6/iSCZHksIfUkcBORakrSMzp485WKNbN7BMAyiOAYZY6aSVJt5I/0Kal6MjJMaDzed5MnEMqnjkFIS1kTzhyEKY0YPlLAcg7bOTDMn3vdC9u+eIFZJUWhLexqBoK0jjWWbSKnmRODm+3GPo0PHaQhoIQSFSZ8nHkoaQu3eNoqMFUIkgn+wEtLVm2V4oEDPulZkLBnYO8mzTw1x7gVdbH5ezxy3E5Rix9bheaPk0ycZrR1pvGoISqFQyXVC4aatpC4GaOvMzBAkGzZ3MjK4l+KUT+BHFKc8lBK4KYswjLFtc8GIZCpt89JXbubxR/pOSzebU4XlTvXRzEWnP5089OqU5kRwRnxSm44czwwzMVymtSNNe2eGwJcEtYg1G9qaXSKbPsi1iO61rUyNefi+roDVLA05LZAlJZSKATufHqazJ0e+LQUIyoUaSLBdgZQCkBgmiZ0lidWklCT+72by45rJOUl6TySxTJNIxSiZ/OA2Un9s28S0k2ZJ+/rG2XjBKizbpFyskc7YpLNOM7KvlGr6r88XgZvvx73hCjM14TWFvFIK27EQhuDJn/ZjO4mDDByqTRnqL5JrdZGxbE4CAPbvnqRcCLjsJevZcnHvklJ0pk8ybNukd10LhQkPFKw5q4VqNQIFlm3UU5SMpiDxvZC+bSOMDpbx/YDAT9KdTMvA98Nm91y73rxqPtJZ94QI0eUuTNWc2UwXmKBXnU4ks92/OrtznHdxh/68a46JM0LMQ/IBchyLnnWtMwSK41pk88lzjR/SIDjkg9zRk2VwoKgdbTSLYtogMIgi2Wzs1Ogi7FVDRg4USWdthCFIpWwMS1AY85Aq6Qwbx5I4lFiWAQiUijFMMG0DGaukE6shsGyDfHuK4qRPFEksUxDHyessO6n9aBTgTo5XWdWbJwxiEEkkuzDhEYVxPXK+cBHsfNGjlrZUst+xalPINzznGw2h2joz7N8zQbUUJOlIBpgGrD+3fcYkAJJI//TCr8Pl7fpeyORYhcKk30wRsm2TjlVZ2jvTbNjcOcfvviFIGhOFibFKcs4xQBKRj2NF4MekMwaFCY+OVdmTGpHUreU1y810gWkaNrab1hPKE8j01anT1VlFc2pxxoh5WDgvUMYzG+X8/IG9TIxWiEKJkkr7zWuaiLod5XTXGVQi5KWUmEbiVoOo586TCPsoklTKAXEkCTJR4r9ug5AGlgUKgyiUTZtJkWSbIGNFHEuUalhSJvaWbZ0ZqqUaUT31pdEwSSmFZRm0dKRwHBM3ZdHansK0TWzbbKamKClxbWvBCNx83vGNbR6+dw8TY9Wk46tjUpjwyLelyORsglqEXw6RcVIJnKQICarlWn0iITFMgZMycdPWjFWBxfJ2G4LXss3kOoUxXjWke00eyzQW9LtvPN5wsYmjJAWo+Zmurx7EcVKDEAbxSY9Izp7EyFgyOV7lge/3cdbGDi2qNCeFhsDU4lKjOf04o8T8UvICfS9k6ECJmhdiGEZSaKfRNEiyYmZgWgIhElFtmEk0OgpmbqRUXdwLiKO6A40QZFscopqktc3Gq4ZUSrUkPz5lEIUQBknUXSHrnV8l1XJANueyYVMHE6MepaKHkoeKu520RXtXlu7e3Iz0FSkTJ5zuNXkK41W6V+fJt7qLuLccih5NTwNpaXcZOlAkDGICP6ZcqlEu+lzz3zZz37d3ggDHtYFDuf77dk3ipJL8/ChKzkEAo0px1qaO+vEW/nw2BK9jWaxe39K03xwbKrH+nPYZNpnz2Wc2JgqWbRCFibNQFCZpNm7awrKSSVhre+qkR8SnT2Km1yPEkWJy/MRaBJ4J6T1nwjkeCfp6aDQrjzNKzC+l8GRf3zhtHWmGD0QopZII44ppq6U5Vmas0tQtKGWssGyLs89rZWBPkSCImLcVmzj0P5mcQxhEeOUQN2VhWSa5vEF7VwalFIEfUSr4SXdiy8AwDWQcE9QFdHevQ2dPllf+t4v5+cN9PPvkIHGsmtsXxqtc9KKkwcTsiHVre4rLX75hST/gjTzz6d1fx0cqVEoBTr3RUyNtZmSwTDbvJvaXscKo56v7XoTtmrR1ZBgfrRBFCtMCvxqBEEyOVvG9cNHP546tw03Ba9kmbR1pBvtDLNtASpqi94LLemc0hWqkrOTaXKRUtHWkGSjWiCJJFEtMMxH3bsqlvSvDFdecfdKFzfRJTCMVKTlP44RaBJ4J6T1nwjkeCfp6aDQrk2UX8//xH//BvffeixCC9evX8853vpMgCPj0pz/N6Ogoq1at4g//8A/J5XLHfKzFluEb+F6E4x6K/jV/WTWa2SjASKLxSsUMD5SxXRNhKHwvOlRnIUBJ1cjoAJU4roRBTBzFKAWlYmL5eFZvUgjlpixGDhYpFWrNiHypGIMCwxLEUjI17pHOOLzgxWdx7pYuHvzhbuJIYjsmLW0ptj8x1PyRbkTZG1G5hdxipjM9zzzJ248YGihSrQSYZiI0M9nErUcpxdC+AvlWl6AWNRu0QdLLIZW26VnbAgIKE8lKgmEZrF7fimkaTbG60OdzdtR+asIDDnW6bYjeJ3/aTyrjzMm7RyUTA8M0SGdsan6IYdjYrpmkQYUxQW2uHeXRciTRz+kOO1NjXuLIk7Jp7Ug3z+FEWASeCd7iZ8I5LoXG/bh/9wRBENPemcEwzAWvh47eazSnF8sq5icmJvje977Hpz/9aRzH4VOf+hQPPfQQAwMDXHTRRbz2ta/l7rvv5u677+Ytb3nLcTnm4WzRGqKhgZuyCLSbjWYBlIRYQhwqlAzq6SWCdMamUqrfR+pQnr1tGxiWoFaNMM0kfO97AaZpksk7jA6W6Vmbp7U9Re/6FgpTIxgkRbSNTqmGUoyPVJgYqfDEI3s57+JOhgeKTWeaBrMtJ6cmqnME/8RodU7Tp8aP+ORYBcs2m3nmybkoglqEjBPP+2RiYifHFXDpi9dzz7eeRSIxjKSOwDAE3WvyzeuVzbkopUhlbGzbrB83ObeFPp+zo/bTi3obNIpwMzl3xmsbdTGNicLESIXO7hy5FpfRwXKzKLdSSiYvxxqlPJroZ2MlR5gCGSUWmw1OlEXgmeAtfiac4+GYfj961aiZyrV6fQuWbc65Hl6lpqP3Gs1pxrJ3RJJSEgQBcRwTBAHt7e08+uijXHPNNQBcc801PProo8f9uL4XLtjEJgyipHNkIxKog/OaJRCGCt8LUSoR38I4tLDTCFRLJUnnXOI4plwMm51g4yjGqySfg6kJjw2bO9n8vB66V+dx01Yi5Ot2lVKqemGs4omH9+J74WFFi++FPPiDXdS8EBkr/GrI8IEScSybTZWmN2Sq+RGFSZ/hAyWEMa3ZmlLNolxQhKGkXKwRRjG961to68hw3evOp7M7i5uy6OzOcsP/7wJSqXpxbDWgWPCoVgKy+SSqP12sLvS5nN1sqLU9RfeafHMy0NhPJmcv2JClMVHYeEEXHauylIu1WRabZtNd51hYrF37Qts7rsWq3jxnb+ogk3UwDUFhwjuhFoFnQvOaM+EcD8f0+9Gyky8iIRqrW3Ovx85tQ0d0/2o0muVnWb/ROjo6uPnmm/md3/kdHMfhkksu4ZJLLqFQKNDe3g5Ae3s7xWJx3tffc8893HPPPQB8/OMfp6ura8bzlmXNeQySyMOTP+3DMASm4VDzFM8+Oc5LX7mZri6XwQ0V/KoijiTZXJpysdZsmKPRzEs9hSaOFDUZNi0iZV0ECwEpx8Q0DSpTNYIgQtbz72UksVyLKJAIYXLu5l7WrV8NQOfrOvjFE/2MHtxOJGJsywSVFMY6biI+x4cCurrbGR8pE0cx46NlojDGNA02XdhGV1cXTz26D9Owcd1D4lcphV+VyMjkwHMVdm0fJqhFdHXnsB2LbC6kWvZxHBsVRwgBfsknk3EIQ4njmDQKB1paMlz18gtIZ126umDTeWcByWdt57YhMtk0fc8M4aYcogjSaYvCeI1cLovjmLzgys0AC34u09kk2t7Z0cHObUPYlsvA3nEy6TRuym52Zn3JK87n8Yf3YUyzp5RS8YIrD+0je2We/7q3D4GH4ziJxaZSrF7bge1YmIY97/cGLPydMp1dxiS53Nw4yUL7nb19Op1mfLQMwPqzuznvwt7m2I8njeuw2LVabpZyvRfjdDjHE830+2v1Wof+58YRQmAIk1QqPed67NlWIJfLztnPYp8LzdFzrPe45shZidd8WcV8uVzm0Ucf5bOf/SyZTIZPfepT/OQnP1ny66+77jquu+665t+z7bQaFluz8/+CIKJc8ilO+UShxLINWtpSPP5IH1su7qVYKNPa4TT30/+cQRzpVBvNIkyb6yUiXaFIcsWT/HGF74cYoi4oZm0vlcJxTcIgJFY1/uveXxy6X8OYju40QweipguOUjFBTdGztpWxkUm2XNzDnp1Fhg+UDlkcKsXeXUOUy2X6d08lefmmwDIPCfpSIWT3Dp+u3jyFyWQSUCxUkuZJrqIwUEVMJV7ySkniKKalI0VbR4Zysdb8/KxanaXilah4pea+py/vT4xWEIYiimPWnt1CpRQQBjGlYpmrXnkuFa+UROJ9b06qUONzOTt9JZ23GBwYp3ddK27axLQET/xsN6aT+HrGMc1839ljO//STqYmCk3P+mzeZfDABGEQ09qeYqA/N29KwVJs+2JZo1yeex62m573tfNtn2u1ae9Ms/acudf1eHL+pQ3nn3DBa3WymC9Pe9361cdsk3gqneNyMPv+6uzNMDVexTQVblrNuR5u2mJkeGLJ96/m2NBWoCef0/War1mzZsHnllXMP/3003R3d9PS0gLAlVdeyc6dO2ltbWVycpL29nYmJyebzx8N8+WvHtw3lTTcsYxm23mvEjZ/vKcX24VhPGeZVqNZCnLa/E+pJHE+iuvh+Fm3VBjESTOnKuzaNsqq3jyGIRjYO8nkaJV8m4vrmgRB4q5kGEYiontaSKVNUmmbts40xSmfOFJYtkG2noNfrSQrBUJAuRiQa3GwTBMpE7/2rtX5GbaNAhgfKRMGiW0j9WimaZlc+IIeKuWI0rSJcL4tRb51bpRz+vJ+FCY59EopKqWArp6koN1NWfN2d20wPVVo+v68asDQQJGwFlOrhbR3Zsnk3BkuOIvl+KbSNle98lyeeKifKJaMHCzVE9cFlm0eU+78kbZrX8727oerITpZLFRn0PnLHce871PlHJeL2feXaRp0dGUXvL/Pu7CX/XuGl+V+1Gg0R8ey5sx3dXXR19dHrVZDKcXTTz/N2rVredGLXsT9998PwP3338/ll19+1MeYL381DCJqfmI9WS75TI5XmRyrsG/3eNMiT0lFrRZxcF+BKNJdozSHMJb4qTGMRB8KUbdT7MzU/eDnbqtkIv6VgonRCgf2TXFg3xS1upAtTvmk0g6plIWbtkhlbDq6c5iW2fyRlTGs6s3Tu66Frp4clVKAUW+U1NaRxjRNsjm7bhuZdD89a1Mnbt1isq0jnfjYVwJGh8pUyzUEsO6cdlavb00EuDAY7i8m+f2RxKsEDPcX6Vl3aMLdyHvfvX2s3nwtxrITIS+EoFaLGBsuM9hfYHKsMi0vfvH85obY96oBe3aM12sMFJOjHvt2T1Dzw/p1X1qObyMPPw5jLMsgnXVYvb4lacB1DDnCs/P72zvTh51YHMn2K5GF6gx2bhta5pGd/hzp/ZXOumf8/ajRnG4sa2R+8+bNXHXVVbzvfe/DNE3OPvtsrrvuOnzf59Of/jT33nsvXV1dvPvd7z7qY8wX7XNTNp7nUZhMOlI2REZx0uPRn+zl8qvP5rKXrOfBH+2iWq5hmIJYR+c1deQS53ZSJt08rYxNOmMxNe4hTBLLyoZN5TRMKynGjCNFqeCTSttkcy62a1Iq1JBS4boWmbyDihXnnNfBVS/f3Fwen23fGPgR5VKNOJKUSzUyWQsn5WAaBhsv6Go2V5ocn5nioeoRajHNljUKk8LcUsEnnbWbXVRd26KlLcXwQJG2jsyMCCsCvEqAVwlZtTqHVwmJ4piaFyGzSYfY6VHww0WoG+c3NFBEoBAiSV8ShkAAQwNFzjkvyYNcqmNJKm3T3pWd1wHnWBxPjjQafLyix6erpeBCqzJeJQDm5m9rjozluh81Gs3JYdlL+t/4xjfyxje+ccZjtm3zZ3/2Z8dl//N1lbRdE8c18CqJKjNNA9MS2I5Fccpv2vmpSJFvTRFFkjiKdPMozSHmEePzEQQxphdQLvj1tBWTUMUwqxeZMJImQajEUz4KFcqFOJZ4lRDHMeoNjiSGELzs1Ztp68iQzrpNMT9dDMtYUpzyqJQDTMsgDGIqJUE273DR5WubP9TTXzM1kYj6TM6lzTET+0dgfLRSt2dNHHSiQKIUTWs7YN5UmNaOdGLzqhTlYo2etXkO7J0km3NIZWzaOtJYtjnDQnO2z3zPupbm34YlCGoRYS1uCnmlwHEtZCyb44UjcyxZSmfo04ET3RDoRE4UFnoP0llnkVcdHafrhEej0WgW4vT6tToK5ov2tbSlqHkhUaCwLBPqrs6pjE0cJQ1/fC+kMOVRLR/yCtdoGhgCmgH62fdGQ+iLJH2mWokwTYFhCgI/xrAS//XpKAVhTc6cJAjwqyEKRSafQtVz1+NY8eRP+7nqlefO2Mf0pmj7d00gzCRVIWlYZSBjSaUYEE5rjjT9NRMjFdJZp+ndnkTAoVyo4bgmCEGuxam77iTiv6sn1xS+vheyf/cEXjXCsg1aO9L0rmuhMOGBglW9uXoqzczLFceS/bsn5oir2eJU1htvpXMW5WKAZZlNwV2c8knVJxZHmuO7nDnrx5PFGiQ1VmGOVsCe6InCQu/BeRf2HtdCVd0BVaPRrERWvJhfqOtr3zPDbHt8iJofYVombtrCEAKQDPZP8szjB/GqiSe3QufMa+oISGcsVKzqxawCKeWMXHjDFPWi0STinjyXRJGVgjiUM/YnSB6X00S87RhJLrdrkm9NhHy5FJBvcZCxojDp88RDMwsEp0cck94IAte1iONG91MTyzYYH67Oe2q2bRCESSFupRRgCAhqSUR+PpGfeOQn++5Z18ITD/UT1At5ozDGq4b0rmuhY1WW9s40Wy7uZcfWoRlpPWEYM9hfwHFNDuybIgxi+raN8rIbNjI8UJwjTm3H4pzzOtmzfQJh0GxMlc07bLqwC9O0jlisLqUz9KnK9Pd85ECRbIuLYRxyKzIMQangH7OAPdGdVBd6D6avPB0PdEdYjUZzKnG8VgpXvJiH+fP/Nj+vh8kxj8GBAqEfUy0FKKUwbUEcQRRE1GoxSut4TR03bWLbJkKAmTbwKhFKJk2fENMaRAnAFChU8/6RCzibNopp1bTnTQMyeYd1Z7UxOV7FMAx8LyTf4mCaZr3BkdUsEOzsdejbNsLubaPYTlJo61UDKqUahjjULMYwBKZlzFhJmB6pzLa4TO6dZGSwRC7v1tPPDNKuTb4t1WzQ1LuuhanxKo5t0t6ZbkZ9hSFo78ww2F9MmjEBU+NVOrqyzSj37Ajs1HgVGUtqniIyZd1dKmlw1buuhTiWTIx6Teec1o40puly3evO58mf9tc7vtpc+uL1tHVkjvq9PR1zhGdHmYMwprC/wJqzWpvpT1IqquWAVMY5JgF7Mjqpnoz3QHeE1Wg0pwrHc6XwjBDz85FK21x8xVqmxivEocS0DKIoolwMiEOdU6M5hGGCbVukUhaYEHoxCklLm8v4SAWlwLIFcayQEcQojHqKzbxMS8Np/rfxlAFOyiKqSYoFn/XntBMGkpHBUrJKVI/wt3WkMQxBcbLK/j3DTIxVkLGi5kUc2DdFHEmUVIQyGUscS2zHJJ216d3Q2jze9EilYZiksw41PyIMJdm8S1tH0lRmarxKV90ucz5ru4ZIMgyT1etbmJpIBLhjmzO2mx2BdWwTmXXqefANsWkQx8kxpyZ9DCGaFrLVSsCWi7pp68jwildvOe7v9enE7ChzMokrMDleZVVvvrlqkmtJzXEJOlIBu1LqClbKeWg0mtOf47lSeEZ+gzWWNfbvnsC0TM7e3EkYxuzYOqyFvKaJaSURdSnrUXcBMlTYKYM4VJSKAUIIZKyII9XMBVcSFmsxZhhJ0XXD8rRhXykEOCkTs+4BPz5cIZ2y6T2rhUrFoVIMsB1rRuHo1GQVUbefbIjhmhfiVZO+CQ1rSykVbspi9fpWNl/Y3RzL9EhlFMaUCzVQyd+5Frcpym3HINviICM171LgdJFk2WYzl769Mz0nwjA9Avv0Ywd4+mcHiKPELjOVthAiSaeJQjVnspP8PTOyeqYyO8ps2yar17dSLdXq/v3Wgo5FRypgT0RdwXIUoq6U+giNRnP6czxXCs8oMe974Yx0hCCIUbFisL9IECTdNZfqUqJZ+cRRIrwty6ClPYVlmUxNVqiVFe1dabxKRFD3Nl8ojWY+rHqNRuJak6RymZaBUhLLStJogiDGrKe+VEohjmPhrrKwHWuGCGlty1AuVw41fBKCoBYjSGb7SQ514pDjpmwuv/rseUW4jCWD/UXiWBLHMUrAnu1j5FrdenqOoDxVW3D572hEku+FTI5W68eUSCko1iJyLSnybSn8csCas1qbEwrLNpKVgujoPqArzcVkviizaRqctbFjRlTneAjY411XsFyFqKdzfYRGo1lZHM+VwjNGzDd+PKanI1TKAamMhWkYeJWwLnq0kteAaQmUUjiOSWdPFse167aQBrajME0Tw0xE85HO/izbII6SNJbedS3kWh12PTOKVwnxvJA4SiLSmXYXRSLAHNcim7dxHKtp04hSjA6XKJcq5FtTeJUwGYtUNBya0lkbs955Nd/qzhEtDaE3OV5FiKSVexjK+nJE8rlJZx3aOjPNRkrzLf8djUja1zeO41qctbGD/bsnUVJh2wZuysAyDXo3tFIpHuoWC0f/RbcSXUyWKtKPl4A9njnty1mIejrWR2g0mpXH8VwpPGPEfOPHY3o6QiptUfMiMlknKVoksb6br0On5szBcgxyOSfJOwfau7IUJ33KpVoSSSfJQU+lLYpTR3azmDZ1VxuFYcLFV6wFYMfWYWq1CBkfSi0JajEH9k2xdkMbtm0iY9hycS9TE1Ue/MEu4ljhplwqpaQxU/eaPKWCj5O2UFKRzjlNIS+VorMnw9M/H2CovwgKejckKTeXvWQ9D3y/jzhSpGyb7tV5DuwrEIcSwzToXdfSLH5dbPnvSEVSY4kxnXE49/wuCvUIfDpjcdlL1gPwxEMLf9EdSaR9JbqYHIlIP9UErC5E1Wg0ZzrHc6XwjBHzjR+P6ekIlmVi5QyclEWnm2X4QAmiI8iX0Kw4Gi4scZykXKUyFvv6JvC9sOnZbloGlZKPaZmH3d8cVNLsqJH3vv2JIXKtDtl8imo5RCDqxYqKKIwoTfnsCydobU9x1sZ2fC/kJ9/rozDpgRJYdogwkh4JtWrIpgtW0XP9Rrb+7AClgk8cKUxLkM7YFCZ8xkerdQtWKDw1zNRYlcuvPpuzNnbMyKtu60jjVQLSWacp5I93oeD0JUZ7gTz7hb7o5ou0Dx/cS/uqzLx5/StVPJ5qIn2p6EJUjUajOX7f4cZxGMtpQdKsJhFRjcioUgonZdHRleX6117I816whkzWRhg0/2nODAzjUBGqaRnkWlxSGZuoJgnrkz9B8pwAfC+mWg6Wbl0qwDQFQihkrKhWAmpeyMhgKSm8jiWOY+G4Fm7KSqLpMcRRklfvVUKmxj22PXmQybEqMk4aSEWhpFLyqRRrSXoMh5yasjkHyzJwXZMgjBnsLyZNqFSyOmUYotnxeMPmTpRUBLWIseEyfjWkWgnI5JIOnCeiULBxzIbTynzHaHzRXXLlOrZc3NsU57Mj7XGcOP7s3zVBzY+YHPd44qF+fC+s78ea4+iixePysZT3XqPRaDRLY8X/kjWW4ksFn7HhMm0daVavb2FyvEoUxJy1sZ3Nz+shlbbp6MrQ3pWltV0Rx5LxkQrR7HaVmhVJ04lGQViLKBY8TMskqEVYlgGWqG8nqfkhcQyIZBLQFPQLZNwk+ffJBBKVRN69SggKan5MHMUI00CIZAdCJH7wUSgxTYNUymLNWa0YpsGebWOYVpJzD6CkJAoVxUkPgF3bRznYX0hel3GwXYvB/gKlKY84SsbgVUNaWlNEkcSrhOzfNcGGzZ2ce0En9317J0EtRilFKmMz1F9gw+ZOOroyx71Q8FiWGGdH2gsTHoYQhLWYseFycu0sQd+2ES564dpldzFZacW3x4ouRNVoNJrjx4oW816lNmMpvrUjzdR4ld51rWy6YNWcH48NmzvZ8fQw5ZLftOfT7jZnHnFM3dIxEfJBFGHZZt0pJiJuZGYoEnFYz7aZ7mgjDJrdXQHclElQi5OIepykvkiZrA4Ztknoh5i2iZL1ug2ZCJ5ci8vaDW3NJkCxVLhpi0opQKAIA0kUSUxD4LgmNS9iYrRCa0eG3rUtTIx6KKmIQpVMGupdqibGqqTSVuLqFMb87P69TI1XMSwD6UegkglHJmczPlLhshefmELRo11inJ2mEYWSWCYrHhmp6r70it3bRtl8YfeyiseVWHx7PDhdU4Q0Go3mVGNFJ5Ls3DY0YynedS26evPkW90ZS/bTybW4yFgS+GE9knqyR605WRiL3P1Jg6YkNcayTcIgxvOCxPu8TsPuXMbThLxI3GrSGRvHMerbJV70li2SKD9gmEaz0DqTsWnpyJDO2Kxe10quxaWtM01nT3aGkJdS0dmdQQhBNu9g2SZRLDEMyORsDMNAiGQVoFysAYnIDfwIyzYQhkCIZHVBKUUYxrgpm7bODKWCT7lYS5o3AaKeYxbWYuJIsq9v/Phc9OPE7DQN0xL4lYBU2m4WuAPYjtkc+0IpOyea6SlBURgzMVphZLDEw/fuaaYBaTQajUZztKzwyHywaNHb7KXvIIiwXYtMLkWl7BOEuhh2pWLUI+cLIUTSwMgwDJy0RTTlo2bXStYj7033IwOyOQfTNJCxJAoTMemmbGp+CBi0rXKYGveTaLppkGtJtldK0dae4frXXQjMjObCoZziF7387GZxq+vWI+5xRDbvTju3Q5ZMlp10UzUMkeS/K6iUk89FNu+wZkMrtm02XZ6iKG5eGCESq1bbMU+5QtHZkfazNrY3G2RBvSaGpCvqco+9kRIU1esWktoMQWHS54mHdIReo9FoNMfGihXzvhcyOlxkZLCA7Zgzumam0ta8S98H903ieyGVUkAYSm1RuUIxjMSlxq8uIvJEEp1v70pTrdSwLAMlVVMYx3H9/qjfI41gcBjEZNqdejTcxElZpNM2ubxLHMd41YhUyqJWizEMqJZDci1J5W3v+pbm4RdLC7nimrObj7d1BgwNTDaj0UqppIjWNajVIuJIJuk9SiafActEGAInZZHLu02nGtMSZFscihM+kZL1CL/EME0yOYfJsQpPPTJwSuU2z5emsX/3JHGksGyD1o50UjtwAopcjyQHvpESNDXhNYW8UgrbsRb17tdoNBqNZimsSDHfEOqO4xJNcwPpWZvHNI1mi/PZbhjlYkClVAPB0l1KNKcdUoHvRyxW26yUQhiCaqVGzYvJtbj4XkS54BNFCtNMehY0tyfJWQuDmOKkRzpnk8+kcNMWgR8Dipofo6QknXWxUzFxIJFSEUeK1We1sPl5PTPGsFBO8fTHs+k83/vW4zNsKLtaU2y5uJtHf7IfKRWrVmcpTHj41Yi2Lpt13e2MDZbJt6WS6yEV+dYUQkBLW5qB5yaJ46Qbbe+6FsYGy/Ssb6HmR6d0vvfm5/VQLgTNz/WJKnI9XA78bKHfs66FidEqYRA3hbwCsnmHidEKEyMVgGWZJOnCXI1Gozn9WZFiviHU3ZRN77oWChMeYRAThTEvevmG+g/uITeMMIwZeG6SWi1qRuN186gVjAJ5mMwLxzHJtjhUigFBLSSKYmzbwHbNJI98VqfghqtNkpN+qDFZ56osAFMTHtVygJu2WbOhFWBGk6Qrrjl7QRG1mOBKZ90ZkfrG8/v6xunqyTXv8VW9eSbHqziOyep1LVx61TqGB4ozXgPJZyeXdykXfXItLuVijZ71LbiuVT/PU7fZ0skqcl2sAdWGzZ3zCP0qF1zWS80PKUz62I5FNu8wNlwBpUhnHSbHvZM+SdKFuRqNRrMyWJFifrpQbzSjAXBTVvNHqrH0HceSoYFikm+rwLIFYagLX89kbEcgUUyNecSxTAR6pCj704oVVeJi0yx8VYcsKE3TwDQEPWvzGKaBYQg6VmWRUtHakW6mtszXJGk2SxFcjUh9Q/Tv2DrMyMEimbyLUbfasWyTVb153JTVFOFtHZk5x5st0J96ZICaP3Pmcyo3WzoZDimLNaBaSOgPDxS56pXnNt/LidFKPVogaOtIL8skaSV2xdVoNJozkRXpZrOUBjENN4yp8SoCMMykmtF2bVJpE9M2sOxFKiQ1K5Y4VkmzqEAiZSLYw0gmEzwFRt1fXhhGUgRrJLosqk8CLSeprm3rTJNtcXBTFu2daV52w0Ys05jTKKdnXQs7tg7x1CMD7Ng6NMPhZD7B1ciznk5D9E+Oe9T8iCCIGewvEE4r4j6aJkmnc7Ml3wsXvK7HwmLXZDGh31g5aO9Mg4J01mH1+pamW9HJniSt1K64Go1Gc6Zx6v8iHwXNBjGH6Sx52UvW88D3+ogjRb7VJY4VNS/xrnZcA9u2QCniSKJQVMv6R+5MYLpfPEnwdM5KjZuxCWsRhmBG7n0sFX41orUtRaUUoqSakUvtZiz2bB8jjiSd3RkuumIt258YWiT/OiKOJaODFUp1u8lci4NlzRRhs0V/vjXFxGiFfX3jtHVmaGlLNetFjoTlbrZ0tMy3ojF8cC/tqzLISM1JLTqStJzFrsm+vvEZ/vcwc/IzfeVgctxbcLuTwWyv/uUYg0aj0WiOnRX5rd0Q6oP7KgwfHAcFvfU85dnbnbWpg9GhMsMHSmRyDoYhqHkhAnjeC3pYe3Y7j9y3l/GR8sk/Ec0pgWUbhEGi2IUA00rSaIRr4VXCGfUVhkhStQzLaKYt9D0zDMCOrSMUJqtYlolpGUyMeTx873N0dOdwrbk56Rs2dzI6WGRgb4EwiLAdExCM+8kk4fkvOhRpnh5ljcKY0cEyqYxDGMSEQczUeJWX3bjpiHOhT9dOnfMVuI8MligVfFb15vGqISODe1EKHNc6opzxxa7JUic/p8Ik6VQYg0aj0WiOnRUp5hsUJn3ybUk+aqUYzPF09r2QIIzZv3uCMJIYJKIslbFZe3YbtmuzZ/s47auyVEpB4oCig/MrkumCvGEz2fg7jqeF3gWIujVl4hUvcFImUaRAJYWvjmM1OwjHsWTP9jGEaTAxVkFJiKMIx7UIgxphzcR2LNx6XQckwqpUSLoXV6sRQRChJAS1GMe1QCUpPvv6xlm3fjUwM8rasEA0hcBtcbCspOnVkz/t56pXnntUgv50y6GenUJSmPAwxCEHIsMQFKd8ICkObjy21JzxxZyGljL5ORUmSafCGDQajUZz7KxYMb+vbxxjkeKu6cvwbtqiPFIBlRTJyljRv3uSkYMl1m5ow3UtsnmXKIwpTtWW98Q0xx3bNZJUqjjJf4e6F3jdsUY2+ijVc+aVUohk6kcqa2OaBvk2m5oXEYUxSiXRfICp8SqWY1KY9BGNLlMookhi2wZhmETOpyOlojhVpeYnNpeGIZK5gQIlFS3tKVDMyG2eHmWNwmTyEcWS2FNEpjzjmhTNTiFpXJPG+wLMsBZtcDxyxpc6+TkVJkmnwhg0Go1Gc2ysyAJYOHxx1/Rl+DiUOI6F7ZgEQSLGpJRUywHDB0pEYZw0/NGt11cMYtqtYVoGhmFgWknHVsua9bFIejrhpEwMU2BaJpmczVkbOzj3/FWksw5GvWNsFMWEYUStFjE6VML3Qto7G64xjS5TAiUVSiVuS6YpZtR3hEFEcdKn5kXJHEIqpEw6sVq2iRAC0xIzcpsbUdZsi0NQi/C9kCiMEUJNa1Jkzls8uxJpFLg3rqtpJde4rSPd3Ma0kus4HZ0zrtFoNJrTjRUr5g/nwjFd7Dt1D+04kiipCGoxfjUiqMWUiz4H+6eYGKkS1nQnqVOeadpMiIU3UypxpLFdA1MIMjkbN2WTztikMjaNitdmyo1M7o+OVVlW9eRYf24HSgnGh8uJyHaMRGSbJqm0TbUYUC74yWRRKnItDoaZrADEUZx0kJWKXKvLy27cRHtnuul609aZxq1Hzt20nbidKEUYxggDpEqaPM2X21yeqtGzrgU3bRMFMeVi0OxW27BAPBPcSqY7x7gpi7M2tjetQiH5LmhpS5FvTS1aKK/RaDQazanOig1BbdjcybNPjiOlmre4a/oyvJOyyLW4TI5XmvnRwhAIA6qVgGolSETAPK4mmlMIAZYlEotIDuW8245Rt448NBkzDOqJ8mDaJmef18ngvilSWZuxoQqCJOUmiZon0XGAoBaRztoc3F8gnbXpXp00Y4oCSSZvE0UxlmU2o+FhEDMxUqGlPcXoUBnDFEhVXw0wBVe98hzaOjIzPN+femSA9s4Mg/1FDEOQb01RrQSEYUxXd46157Sy+Xk9c1Jlms3SLIvedS0MPBfjeyEylqw7px3LNo858nw6dQydnUIy39h9L+TJn/ZTLYdkcjaXvnjlpyBpNBqNZmWxYsV8Km3z0ldu5vFH+uYVHtNzjNs60lTLAaZhIA2FjFWSL20IDMNIBH4jQ0Jz6tLwep9FFMkZ6RSGKQ6lwCuFjCWua7F6QxtxGOOVQ1SsEJYgCmKEkCAa2yqUUiCgrTPTbMYkpWLgucmmkIck796yTJyUhQDaOzKEYYybsnBci3xbiqkxj541M52WGhPN1etbmGp0ic0mzksXvXDtgqc/u1naunPaGRooYllGU8gfS+T5dO8YOp+43/7EEKmMQybnIqVi+xNDp835aDQajUYDK1jMQ9LqfqHirtlODlsu6WHPsyMc3Fus50gnUVsZS2zHJI7iefejWT4MY6bH+0IoCfE0kS/jaYK//r8H909R8yPSGZvO3ixeNUgKUwUgBEooLNvAcS0CP2LV6nyzk2syFkEcSQzn0GMNLFPQ3pUlk3PnPFcq+PWGRocmnI2JpmEazS6xSio2X9i96HnOLvq0bZPuNXni+gTiWCPpK61j6Eo7H41Go9GcmaxoMX84ZkfqxofKFFpqBH6EUkmqDQLiKMa0TKSMUTpt/pQhk3OoVALUEuZZaoFVFanqFqWDMVJJKkWLNa5BFMlE5yswjWRil8u7ZPI25UKN/bsnOWtjO+mMk+xHKjq7M0xN+iQZPEmajVSK3vUtOM7cBj21WkRhwiOKFHEsGdg7yfanhth44SouuKyX4YHiMTczskyDy1++4bhEmldax9CVdj4ajUajOTM5o8X8dHwvxKsEzTSKRoOeOFbEUZKLreSh5kGa5adcCo5L6pOUCiljTNMAoTiwr0i+JUm7UAgCP8QWEAUxUSBxUxbFKZ/9uyc49/xVmKaBkooXXX02W392gKkJj3KxBkqRzTusP7eDVNqeI7QL41XaOjPEseTgvgI1P0RKxbbHDjI1VuXyq88+IhF+on3DV1rH0JV2PhqNRqM5M1mxbjZHQiMXWCrI5p3ECSRUSa6xZZJrcbFtC8edm0KhWUaORcgLMMym7XvS7Mm1MM0ktzyKFKm0TVtHOsk3jxObQyGSOoqWthSmaVAt1WjvTHPZS9bT1pHh4ivWYgjI5hzaOjN0dOfY/sQQwAx3lfbONL3rWnFci4mRCuWSX3dTglotZvhAib5tI4c9Da9SY8fWIZ56ZIAdW5PjbLm4l0uuXMeWi3uPa+73bLvH0939ZaWdj0aj0WjOTHQIikO5s22dGXwvyZs2hCAKY0zbZM1ZLUyMeviTwXIPVXOcME2BaRmEQdxMp6n5EZAI9pofEEeSSjmg5kcEtQjLMomlxDQSG8q2zgzda1pmpGoNDxTp6s3PifY28rCnb7tj6xCT4x6lYg1DCBp2SaZpYhiCoX0FNl/YvWCkPXFi6cP3vZNSkHokkf/TwfVGd0DVaDQazUpAi3kO5c7GMZgGjI9UkyisEqRtg/HRatKtU6fYnDaYVr3mgbrDzawovhB1e0qSAllF4lRjGGBYipoHKiWwlJGk3yiSxmGVkGzOQSloaUvNSck4kjzsRo47KmkgJUTy38Y+41jyxEP9RLGkNOUTBjF9z4zwshs30daRWVKX4+MtVJfSMfR0cr3RHVA1Go1Gc7qzYtNsfC/kqUf3NdMP/EW6t6bSFrVaxNBAkeKkD7IRI1X4lZCpsSpjQyWkNrQ5tRGJNzxAHCfe8HGUCHlhzGoiJVQ9rWLmLqQEwzDI5h1E3dVISYWbsUAIal5AFMYYBkxNePSsa5nx+sM1K5u5bRIZ7l6dwzCSlYJ8q4sQAqkS95wolowcLOFXQ2SsqPkRD/5wN74XLjpxaAjqyXGPmh8xOe7xxEP9i34OjhfzucQs1HnW98IZaUInY3wajUaj0awkVqSYbwiZ8ZHykoTMhs2dFMar9S6bEoUCBEJAXC+IDWraZH45EcbMf4ZxSLhPpynOVSLMG1K30QRKCBAmCAwCX87pEmuYydamnUTkpVQYpsA0TGzbIIoUlmPipm1aO9Jsf2KmAF0oD7tnXcu8ojWVtnnZjZs5+7xO2rsy2I6Fm7boXp2nrTNLacqvu2MmA1UKipMeD3yvj8mxCjV/5j3dmDgciaA+3ix1dWI5JxwajUaj0awUVqSYP1Ihk0rb9K5rJZ11wBCYpoFhHBKGUaSF/LKT9GnCdgzctEUm52A7JpYtmp1aZ+lyLCvp2muaSZaNIZKIu1tvoCRE3bJytqAHBIlvPPWJXdIwCtIZm1Tapqsnh+taM+6rRloLBgz2TzE0UMCvBpx7QSfbnxhaULSm0jZXXHM2my5YxYZNHWy6YBVXXHM2+VaXMIibQj6OJeViDSkVnhdh2Sb9z01QqyUieXoB55EI6uMdGV/q6sRyTjg0Go1Go1kprMic+aPxj863ukSRJKhFjBwsJfnx4pDbiWZ5Uaopq7EtkziWtHWkiaWkMO4DKkmNmpUKlbjPCNIpC8MwSGftZBUGONQGdvqBwHZN3LSF7ZjUvHrPAZFsnM27ROGh3JzZaS1xLBk+UAIUUkpM2+TRn+yntSONa1nN18xuTjRf7vaGzZ30PTNST+sx8KshSiUuO40GVuvP6aBULOO2pmbkxS/FdvFE5bbP53c/n0uM9nnXaDQajebYWZGR+SPJW26wYXMnQS1qRjkBUPNHbjXLhAJhGGzY3IFhCKbGq6gYzj6vHcc1Z9Y0CAgDWfeQV/heRBSFlIs1orq4nNNISoBlG7R1ZTjvoh7Ov6SH9lUZWjvSiRhPWVTKNbxqkjcPc9NailN+vbjWQAClqcRysjTlzzhUHEv2755YNCKepOBswk3bGKbAsAzyranESacjDYCbsmnvys6xolyK7eKJiow3agGm23DON0E4ms+pRqPRaDSamazIX81mZPAI/KNTaZv2VRmmJqpMjVcxLVEXQ/UNGlFczbISBDGD/UWEKZAKilM+XrVGHCmECaZhoFS98LWO45pEYYxflaRzBum0hVeNUJFEmCDjJJ/ecQw2Pa8H17XYfGE3AOVC0Iy2m7aJ9GOEgMH+Ij1r85imwYbNnezYOoyMZdLRNZQYpiCVtohCie2YhMGhmUYYxgz2F0hnbGp+tGhEvK0jw3X//Xz29Y2zf/cEQRDT3pnBspOeB4crrl3MzWZ6ZDwKY6bqYy9NesfsfLMUl5ilRvA1Go1Go9EszIoU8w0hMz4UMDYSLtmWT0YKyzLJ5tykgY8C34+QsazbGIoZIlGzDKgk2m3ZRr3IVRL4IFXyvpuWIAolcZyssNi2gZuyaWlPUy7WEAJM0ySdgTCIiSKFIRS2m2yHUlxw2aEI92UvWc/D9+7Bsg3Sjk3PmjyVUpC8Nox5/ovWsK9vnMH9U4wOlwGBlBIpoViL6OrJ0dKWYmo8sTttrCigoL0zA8yfdjOdhjDesLmzmRYDNFcdFhK/hxPUjVQcGctkglRfgQoswRMPnXgrSe3zrtFoNBrNsbMixTwkQuGSy1czNpY9gtdYhEFMKmM3hZ/jGMRR4mzjpk2KunHU8lEvdFVKEcfJpMowDGQs6zaSYFkmLa1pxkfKKKXI5FwyWQcA2zISf/iOFKODpXoufDIRUApa2tOkMg7bnxhqCtlUOkljyeTc5jDSmWR/sZQ8+INdxLGiUq7V+xAohDAQImk+pZTENA1eduMmhgeK+F6EY5u0n3Uoup6cx+FzxecTvy+4cjMVr3RUl7MRGZ8crzaFvALaOjPNdJsT7cGufd41Go1Gozk2VqyYPxo2bO6kb9soURiTa3HxqyFCCFraLDwvpDihhfxyYZjgumZiHaoUUsZIqTCthoG8IqzFzbQRYQhkrHCnpaDYrkkcSQoT1aSwlaSwVQhFHCriKCaOJVPjVR74Xh9nbepgw+ZODEswOlQijhLv97aOdNLVdfcEMk4qcwM/RBkKy7KIY4mBwHIMHMdqTgzaOpJIfKPz63SWmis+W/yms+5Ri/nG5OCB7/c1z621I41dn2ToQlSNRqPRaE59tJifRipt87IbNjajrZlVDumszejBEn5Vd4xaLgyDuuf7ocZPcV1EyzhJaTENMO3EfsgwBR2rMslkrL4PKSUoaOtM0f9coVn+oJTCsk0sy6BUqFEujiXFrQrKpRo7nh4mlak7wwhBFMYcLAeYpqDmhc1xxFGMBGQUkko75FtTSCmJ4rlpWUeSK34iurhOJ5W2OWtjB5Pj3qLONxqNRqPRaE5N9K81cwXT9JSIybEKTsoiCrSYXw6MxPQdpZJCVjeVQgHFKY+wJlFx4kBjWopYQmd7mvMv7ql7rYc8+dN+quWQTM4mnbPp3zOFaRjNOz8R44ngrpZDbEcQhYkHfWHSxzD5/7f353F21/Xd///4bGebM+uZyQyZbGRhlSVICgQCCKleiv2BVYtSbSNXWy5lqVp7Qb1arJd6CW0pVcCCWoTiJSq/S1JRKzayCcE2bIIEQtiykExmX8/22b5/fM4cMslMMpPMdibP++3GjZlzzudz3ud9Anl93p/X+/XC6DFYtKyBoYEinhtg2Qb9PTl8L8T3g3KDqjAE1w+pqo424UJUdWbfdJXx5opPVenIfWkjqoiISOU64oP5veuD9/fmcYs+Wzd3sOrcRfR25sgOuvR2ZlWecoYEAVTXxamqjmMaUb58VP/dJQxcIKoaYxoQwIga8HUNKc6/6Njy7/9x/2ZMwyCWsMkOFko16EtdfvGxLIPAj1brY3GHMAzJD/kYJrTt7GfB0fXlFJTOtkEsy8B1w3KH1rB0UZBI2iRSDnUNSWzHGjVdZTy54qOVjjzQRtlDpY2olWWq79aIiEhlOeKD+W1bu8qlB6NNlAbZbJ6ffO+3UapGziObdcvdYGV6JVI2y49vorc7V97DEIZQ25Ak7IpW5RNJh4G+fLm50qsvddC+e4BV5y4Bou94oC9P285+/CDEMsCJRbntRmhgWgbxhENISDHnRVVtiKrdBEGIbZkUch5tO/tpWVCDZUVdaHu7cuWKpQbRar4TM0lWxWhsTuO6Ph1tA8Qciy3Pt5VXuscbiE1nUyVtRK0M03W3RkREKscRH8znc1650Y9hGPh+wFBfgSAIGBwo4hY9lZifIaYF6eoYtmORronT3T6EHwQkkg7JlB2lzlTF6O3Kkc+7WKaJW/TJDrkMDRR45GdbKGQ9DBMK+YCQkELOw4lFvdLiCZswCGk9uo7m+dX0dud585UufM/Hc6PSpJZtRg2bTAMD6O3KUl2bIB63MQwIDaN00yYkFrdwYlFFpOFa8oRQvyhFT1eOPbveLAX89rgCsfF0cZUjy3TdrRERkcpxxEcFw+UojdLOykLOLTebyg4WMU0wbZOgqKX56ZZM2cxrrSE7VGTnGz14nh91eU3B0EAR27EIg4B8zsU0ogDdMGGwdDHW150jnnBw3ajRU3VtAt+LOr/atkk8bjNvfprGo6opZF36unMctaCatrcGcItR2k1NfQLDMIknLQiNqKxkU1S6caCvQCEfpfpASCxms2hZPQDZgQLJlDOiwdNAX9QFtqmlGjh4IKZcdtnXdN6tERGRynDEB/Nvl6N0MU2zVL88JAyj5j+GYaEcm+lnmLD8HfNYccI8fvlvW8opNrZjkO0vkq6N48QsejpzFPM+phWlipiGST5fwHMp3W3xKBajYD7wc8STDoYBNXVJnJiJ7VgM9RcxTYPahiR9XVmWHd9E285oVT2edMrlGosFD8/12fFqDxjQuqSWXdv7CYMQyzJIVsWIJ6L88y3P76GQHxlgjdZw7ECB2GzPZVfu9vTT3RoREdnXuP8G8DyPrVu30tPTw+rVq8nno1XGRCIxZYObTGMFHvuWo4wnbYIwoJD1Siu/YXml3nIMfFcJN5PBMMpFZPZj2QbxpM1br/fR8dYAxYKHE7MAA7fo4fshuaxLbqgU4Jcq0OSybqkD7NvnKhSiMpMh4BkBtudjl1bXfdcvp7wAxOM2jS3V1GeSrDxrIc9u3IHnRyv8hZzLYH+RBUvrwIDcUJHBgZDq2jjZIRfCkJq6+F7NpvYPuix7/13UBwvEZmsuu3K3Z4bu1oiIyL7M8bxo+/bt/Pmf/zl33HEH//zP/wzA5s2byz/PdsOBR09XjkLeo6crx7Mbd5DPRVFfXUOKtZccz3EnN0dpEgHESvnUvhflTscSJuEoNcMlYloHf83ehgN5yzZwYuaI4w0DvGLAYH+Bzj1DuEWPYiH6x/OiLqu5oWg13TANwiCK1g1jn9XvfTY7DJeLdGIWb73ZS39vnu6Ooaiu/PDnKK2UJ5IOx69soa87F+XAF33iKZvujixV1bGo6+tAgcH+AomEjWWbZIdcfvvULrY830bzgpoRF4JBEFJdm6CmLjHisUoNxEbL3R7uGitTZ/huTX0mSTxhRxeeuoASETmijWtl/lvf+haXXnop5557Lp/4xCcAOOGEE7jjjjumdHCHKzdUYMvzbWx/rZti0ac+k8I0rVFzlRNJp7TqlaU2k6SjbRDDANuxMAxwXR8nZhIEIW5RQf0IBlEu+/Cv5iiZSaXA2jDAssG0LGzbICi9zjQNioFPGFIKdqPqMJ4bNYVyHIOQMOr+6kfvkUg5UVBvmJixELO0ATYc+ZZltmNRXZcgN1gkVdpYmxsqkhtyOWphDbZjlVfKe7uzPPyTLQz1F4nFLSzHxDSi+vFDA0WSKYdiwcModXr1/RC36NO+ewDPC+juyHL8ypZyv4Lhu0Ew/mo2s5lyt2fObL1bIyIiM2NcwfzOnTtZs2bNiMcSiQTFYnFKBjUZooZBW8nnc+SyUa7z7h395aBttMBjeLXRsk1isWhqDAOM0u1sJ2ZFaRuqbTPSPtMx2hYD2zYwMKjJJIg5NnbMpGvPIE7MIgxDioUokDdMykG/aRlvn9qIgnXDMPHCgHgi+g7DEOyYiWWZeMUgiuANMKPi7xhGtOHVsgyamtPEEjaEIYlSLnwuG6XI9HbnaGiqIgxC6hqTbLj/ZbJDBYIAcjkX3wtIphzSNfFyLftUVYxEKgrEPSvAMAw8NyhfLO7Z2T9q0DUXAjHlbouIiMwO40qzaWpq4vXXXx/x2KuvvkpLy+wNSrZt7cIspQHYTvQxgyBgxxs9tO3sp6NtYL/UkPJqY2iQronjOCamGW2SbGyuwi1GDYLMcc2alBlQVROnaX6awAvJ54o0t1ZTl0lFQbthEk/YWI5BGEQXT7Zj4haD8oVBEEDgRwF1Zl4Vza01pfrwNql0jDAE0zZIpmxME4JwuDNs1BjKsEKyWZferix+EJY3tbYsqMGJWwz05hnoyZGui/PCU7swTDBMk0LeJQyiuwS5rFsuY2rZ0QVeXUOSYsEjl3UZ6M+TzxZL9e7n9ir14hWZ/dKIKjVlSEREpJKNaxnt0ksv5YYbbuB3f/d38TyP+++/n//4j//giiuumOrxHbJ8zsMyY0DUYGhwoEB2IMqzjiqThFF98pxbTnMYXm20HRPPNUil44RhSCLl4Hk+8WRUWzw3VKRYOMIq3JRyViwrCpp9N1rxxjh4sR/LgsKQSyoVoy6ToqYuQceuQeobq6KUmVKNfwjJZ6P0k0LBL6+AG2aUwoERBeiti+toaklHlYhe3MMLm97Csg1SVXFyQy5OLErNMQ2DRDpOIe9iAKmUE5WS3OdOQrHgk66NU5tJMdRfZPf2flJVDoRhuUmVYZgYZojtmNi2wYKl9fR25QiCkIG+Ap7rY5gGhmWye0c/za3V1NZPfHN4pVSIme2VdkZTKXMrIiIyEeMK5t/5znfyV3/1Vzz00EOccMIJdHR08LnPfY6lS5ce9gCGhoa4/fbb2bFjB4Zh8MlPfpL58+dz880309HRQVNTE5/5zGdIp9MTOm8iaVPIRVGb41gkkw6FnIttWyRSDnUNSUzLHJE3P1wpIlnl0NU+RBgEGKZJQ1OKvm6PRcvq6e/JMzRY2D8hew4zjCiVxaDUOdULMGIhxbw/zjkwsByzPO+93Tl6u3JkB4u0LKxlsL+A5wbU1qcIgiE8L4jOHR1KGILvBVi2ge+FWJZZDsROOn0BhVwpV90NcOIW8ZyDYYS4xSAaq2FQl0kRS9g0zKuifdcAvV1Zmlqq6e3KRo2dMikgumiIxSyyQ0WGN8v6XkAQhMTjNkuPayJdHeek0xeQz7n8+qHXqUrHyA4ViSdtbMskCAJ6u3OcvmbxhOa50irEVFLudqXNrYiIyHiNK5h/8sknOeuss/YL3n/9619z5plnHtYAvvOd73DqqafyF3/xF3ieR6FQ4P777+ekk07ikksuYf369axfv56PfexjEzrv4hUZXn6uiyAIyyXcUlXxcs78sL1TIRJJh6XHZ3j4J1shjFIILAJ2be+jYV4U/DfMq6KrYxDPdUd72znJdswoj72UH+15AeEEau+HpbksFjx27+gvrcRHueh73uonkXII/IC+nmzp9XuVrgzBtIHQIPDBc32OX9kyIgCrro3jeUE5f9t1fXq7slSloacrR219EtMwyA4W6e4YIlkVIzfkYppETaAWpUb8mWhZWMO2V7owrFKVFsckxGDJMRksyyznhSeSDvWNVaTScVzXp687h+cGxB2beUelJxwkqrvn1NHciojIXDWu7O/bb7991McPt5pNNpvlpZde4oILLgDAtm2qqqrYtGkT5513HgDnnXcemzZtmvC5E0mHsy9YUS7hVlufoLm1ekTQtu+GvXzOZdNj23Ecs5yH7QfgxKK0nD07+ulqH8KyzCNmVR7A94Pyv7ODRXw/GFG95mBs28I0oZD3yik1sYSNgcFgf4G+7hyDAwWKhQDX9aIcdWuvSimhgWUZmJZBQ3OaPTv7R5x/3/xtyzKprk3gJKLvNp91cV2vfAcgN1QkWeXgFgNaFtdiWiP/M4gnHN6x6iiOWhQ9l0zFWHpshnjC2S8vPJG0o83RjkVjc5qWBTU0NFVRXXsoKTaqEDNVNLciIjJXHXBlfs+ePUC0cbS9vb1Up/vt52Kx2GG9eXt7OzU1NXzjG99g27ZtLF26lHXr1tHX10d9fT0A9fX19Pf3j3r8hg0b2LBhAwA33HADjY2NI563bZuzL3gHEJWpfOKhreVNsYW8S2/nADU1ad56Y4hjTmjhrTfasEyLeNxkcCCPbdtAiO9CrCZGc0sdWzfvJjfkHbDp0VxkmlGlluHPbVrGqB1N91VTnyCZilHM+6Sq4gQ+ZIcK+H6IE3ew/QDTMjAtE98LKeRdXM8v5dCXhCGmZZFMxli4uBHLjO/3XWfe38Arm9vIDRXxfZ/Xt7TT250FTDzPI99bLG1oNgCT+QsyWLZFuiqOX7TKfy6C0kXB2ResIFkVJzdUKJ83WRVj4ZIGdrzZTW5okGRVjONPWswzv9623/GnnREdPxGN84boah/cr0JMZl56v887zLbtMZ+Ttx3K3I5G8z29NN/TT3M+vTTf028uzvkBg/lrrrmm/PPVV1894rm6ujo+/OEPH9ab+77PG2+8weWXX86KFSv4zne+w/r168d9/Nq1a1m7dm35987OzhHPNzY2jnjsuFMzbNvaxUBfnradfdRmUgwODtHfP8j21/dEZRIJKBZd3KJXXuktFl1ct8j2N4bI51wMc45E8UZUmedgq+yBD74ZYDkWEBIGUXAfEhAG4ZgbYC07KhFZLLgsP7GJ7s5B9rw1iGVG+fPZwWIUUDVXYVkmA71ZXDeIykrudaVkmBBP2CxYWkuhWCAVmPt91wCtR1eRz8XYsP4lCnmPIAiiuvNhSBj4eG5IPGFTUx/D9Yq4HoS4HHdqM1s3t7N7Wx8YUZpNV3c3iZxTPi9U0dud5cc/eArfC3BiFjV1Cba/vmevevJueWPlUG6AodzAhL6OTEuM7a8PldNBhivEHHNyw6ifF/b/My6jO5S5HY3me3ppvqef5nx6ab6nX6XO+fz588d87oDB/A9+8AMAvvCFL/DFL35xckcFZDIZMpkMK1asAODMM89k/fr11NbW0tPTQ319PT09PdTU1EzK+w1v2NvyfBueV71f/uxgf56augS5ITeqdx5ETYpMyyIkJJ9zicUsPD/AM4LKX5kPDx7ID/PcEHy/1B3XimrAF/cJ5PfeFGyCbUVNmBYvayAIoJgPSCQdHMfCMKKgyvcDfC/aSFsslDbUmkSpL150cidus/S4RoIgpLNtANs22fJ826jVSLZt7cL3o5r0iaSNW4xW+S07alKVTMfIzIs2U++dZjXYW6C6PolpGgwNRJsl994cmc+5PP7gqxTyHqZpks+65IZcmlurx6wnP1GVWCGmUmhuZa5TtSaRI9e4NsBORSAP0ep+JpNh165dzJ8/nxdeeIEFCxawYMECHn30US655BIeffRRVq1aNanvO1b+bLomjlsMaG6tpqcrS3f7EJZtMn9RDXveGij9T9KhqlSqMihUejQ/QaXAvVxpZl+l6TBKKeh2zMQwDfp6cjTPryEMo0ZLGDDYF1UEMkzo7YouooabchlEOfKpqkTUfdU0iMctdm2PVs53be/D9zye3rid6poE1bVxTj1rIXUNKfI5Dydmkc+6mKZJdW2cfM7D9wNiMYt586txSp1eh/Pfx7M5cu+LBHi7lGZ/b/6Q8uPHUkkVYiqN5lbmKlVrEjmyjSuY932fBx98kM2bNzMwMDJ14HAD/csvv5yvf/3reJ7HvHnz+NSnPkUYhtx888089NBDNDY28tnPfvaw3mNfo3WvLBQ8fNcnXROn0O9y1IJalhyTwS247HitF8sySCRsfM8n5wXE4w6W5VMs+Jimie8HB623Pp2GO6lO992D4UB+OLfecwPa3xqgWIg2oBJGTxphiGFGXVvDMMA0k6SqY1h2tB/BidkkUw5BGHLsSfPAMEpzbUSVY7pyBGGI70VVcjbc/zJrP3AciaRNdV2i3NkVoj0ftm1yzMnzokDeZ8TK1Xg2R+59kTCcz28YBm7RV9dTEZlRqtYkcmQbVxRy991389vf/pa1a9dy77338tGPfpRf/OIXrF69+rAHsGTJEm644Yb9Hr/++usP+9z7Gr4NOdCXp3PPIHUNSWJxm0LBY8+OfloW1hAEkEjFcIs+y09s4rknd4BhkKqOg1EkN+QCIZ4b1R6PxU0MwyTMB0ygwMuUGe5Oalgm8ZhFsRBdcIzvYCZWpcco/aXhRweVV+RtE4yoRGQYRGPq6chiWgaFnFcuPWlY4HvgOCbZbJHWxXXs2TVAMecRhiHxpE11bYIVJzbzq3+PNi8bhsHQQIGQqORoseCRro4TEPDckzs49ayFbN3cgUF0gVbIuVi2ReuSOor5gELW3y99pqdziL6ePE7MKneG3bfS0b4XCYZhEAQBTtxR11MRmVGq1iRyZBtXMP+f//mffOUrX6GxsZEf/vCHvO997+OUU07hm9/85lSPb9LsexuytiFJb1eWlgW1+K5Py8IaYvFoOkzToOj6PP7gq/h+SOCH+KU65vWNKQYHCniui+0AmHiujz8bInmA0qp2TW2coVIZyXGb4Cq+aVIq0+kTBIy8ExBG9eiHS1F6XoDjmAR7PR+4pQ24QVTysnPPEM3zqxnsLxBzLFoW1YBhsOX5PfT15gjDqMxl1DXW3G/Au3f20/fTVzDMqJxovhTIL1pWTzIVVV4aXq0a7h77+kudGFZ0UeC5Prmsy7z51dilxlTDooZiWebNr2agN49b9HFiNue8e9kRcxtbObkis9Nod5v3XZAQkblrXP+lF4tFMpkosInFYhQKBVpbW3nzzTencmyTat/bkPG4TWNLNdW18ShXOz9yBaO/Nx+VT9wrtcKyTJyYRV1DkrqGJG7Rp6t9CN+fPbnzYQCJpEU265abM03Ze4UQhkEUyA8zwC0G5VX+aGZCDBNcNyil2UT/mEZU4jIIQgwMDGCwv0BDYxXHr2zhpWfbMEyDwI+q0gz0FUgkbQwTQj/q7BqL2wRBQH9vHtM0iMdt/GJAPleIhmNE3+VwMG+aBgN9BZ7duIPuztJ3V/r+Ygk7unBzfVatWTwiUN17A2VNbeKgweyhBr6zNWBWTq7I7DXcvTyAEdWadNdQ5MgwrmC+tbWV1157jeXLl7N06VLuu+8+kskkDQ0NUz2+SXOg25CjrWpEK69R2sXeqRVu0Y+qrwAdu4ei7qazrKxNX3ee0Ihi5ok0d5oIywEC8PaqNT9iGvaZEsPYayylFfwQMO1os2tVTQzbsYg5VjloHg7kd+/ox47Z2I6L6wYYRvRd2I5JqsohO1QEA6qqY7iuz0BPvvyd2DGTno4smaYq7FL6THawQCIVw/fCcv67ZZlYlknz/DTxhD1qgDreDZSHGvjO5oBZObkis5eqNYkc2cYVzK9btw7Lijqn/vEf/zHf/va3yeVy/Nmf/dmUDm4yHeg25GirGpZtUl2XwHEsWhbU0Nedwy36VKUdBvoDOtoGCYZXpWdRLO/EDNzi8Hr4JDNLq+lm1I01NCAkIJG08f0Qt+iPefEw1vWO7wWk0g6LljZgWib1mSQA21/tJpfzyGeLmJaJbZnU1CcpFnyq0jHiCRM7ZlPMRXXkm46qpq8nR9vO/lJzKyOqLx+CXWXS252joamKMAhJ18QJArCdKEXKMKJc/OF9EId7a/pQA9/ZHDArJ1dkdlO1JpEj10GjliAI2L59O2vWrAHgqKOO4m/+5m+mfGCTrXlBDVs3d4xo+GOV8qJHW9U47pRmXnq2jSAIcRyLhqYq3KJHIe8yNFAk8MJJSWExTcCEYJJioqlYiTdKJSQxDCzTwHYMTDNqIFXIhXheWNqcajJcv9IwSwF8GH3GEDAtyik5lg2hDxgQTzh07hkgDCCWMPmvR9+k6Pp4rk8+7xH4IdW1cUzDoK4hSWNztHp+yhkLANjyfBs9XTkCPxul+ABhEH0/vh9g2xaEUJ9JlktR9nTlRtx1icZkTMqt6UMNfGdzwKycXBERkdnJPOgLTJN//dd/xXEq93ZdPufy0rNtUaWSmIVb9OntynL8ypbybcjhVY1TzljAsSe3UNeQYuXqhdRnksQTNrGERXf7IG07BijkPYzhzql711afQHBvGBBPQSLlEIvb5Uowh+tQ8vcP9t7JKoeWBTWkUjbxhE30xyYkn/MICfHdINoE7L2dPB/udcfCsk0SSYdkKoZtG8QTFlVVCdK1CWrrExTzHrkhj8ajqtm9vZ/23QOka+LRBUApeMxlXUKgtiG5XxC5eEWGMAgZ6MuXusdSrmFvWSZBELDs+EaOPTn6vodfb1kmLQtqiCdtTMtg0fKGSUlpSSTtcvfgYeMJfA/1uOkwPGfD41NOroiIyOwwrhDyne98J0899dRUj2XKDKcvxOM2jc1pjlpYS2NL1LnzQIYD/GNPbubNLZ10tmcZHCiSz3l4bjhcMj36B6iqdjhqYTVOzCQWN3BiY09vMm0zf2GG405pobYuiWWZkxbQT4RlR7nnthO9uWmNfN60opX1xuY0Dc1pwhAsyyjlyocEARhmOGYaje0YWLZJbUMCs9TZ1XEsDBNicQvTjBpL+UGIAfheiGkYDA0UaVlQQ22pK6tpGrQsqMGyzP2CyOE7KxAF8KZlYNsGlmURhgG5IZeBvjxbnm8jn3PLr6/PJElXx1l+fBP/7UMnctI7Wyclx/RAgW8+57Ll+TZ+8587y+MZz3Ezbe85iyds6jPJWZHLLyIicqQb15Kf67r84z/+I8cccwyZTKa8aRDgqquumrLBTZbDTV/Y/OxbdO6JNrsCbyejD1drKQX0dQ1JahtSZAeL5HJuFPg6UTpJsE+FSLcYUCh4vLGlAwwTwwyZ7kL1phmtdC9a1kBfd46O3VGXW8uK0lOiFBkT27aoyyQp5F3C0kbgMIi6oYZhgGGamET15V3Xx7LMcklMyzJpXVJLbUOK9rcGGBrM4xWj4H9ooIhlGYQhBEa00TWWiK4mPDfAcSyaW2uoa0xFDb2q42Nu7EokHRqb0/heSBgG+F4UFIchxJM2QQA9XbkRG0qnKr90rM1owAE3uM72TWzKyRUREZl9xhXML1y4kIULF071WKbM4eb7vv5yVyk/Ptoo6Rt+OaC3HZN40sQ0LDw3YMdrPWCEEBoEgU/gge0Ml3GMjjFM8N2AgZ4ciaTD4ECBwI8C5EMywWZPTswsH5KuieM4UbnNrvZBTCtKkRlu0GRakC94vPlKN45jkqyK4bk+phcQ+CGJpINtm5iWSeBHjZ7AIDdUJAhCTAs6dg+ya3s/NbUJ0jUJCjmfQj76PjwvwLJMEimnvAch2oBslH+2LXO/UpGjWbC0noG+fKlTbEAhH13EOY6N6/pRMyimZ0PpaIHvlufbDrrBVQGziIiITMS4otkPf/jDUz2OKXUoNXj3rvc9NFDEsk08N0oEN4xSYG5CZl5VOS87O+SWV6R9LyhvRvXezqSINrwSpZtYtonrRsGsaQaEVinoH2efp+FzGYYxoVx5txgQT0Q7bwd68zQ0RZ9hwdH1vLGlA6+UT26YIb4LdgxyQ0XMmjimaVBTn8D3Qwb7CsQTFmEYzWtuyCNZFSOfjQL54XSR3KCL5wX4rk9dY6pcat6yTBzHxEnY5YuHwA9pbq2mLpNksL9A285+bNvi1w+9zqlnLaSuITXm51pxwjx6O7P0dGXp7cxhmgaWY2E7Jm07+2lZUIPjWDO2oXQ2b3AVERGRyjQDWdrTb6L5vsP1vnu6chTyHrGEhVsMsJ1oBXo4zSgWi2qTR+k3UQ160zTwvYBwjKXyIIj+GQ6Co+A/Ol/UmGr8u2iDILoo2HfT5L5G25zrugGxuIlhQOfuAaqqHc66YCnHnzo/mhcDXDfEDwI8N8B1fZJVDoWcGwXxcZsFR9eRqoqxaFkDyaoYx77jKBJJm+xgsbS6HtWhDyHKi/dCinkf2zKxYxaptEN9UxWti+tIpBxMy6C2PsGqc5ewcGkDb73RR+CHeG5AV/sQG+5/md7u7JifM5F0WHXuEmpqE9Q2JKmuS5CujmPbFgbQ152b0Q2ls3mDq4iIiFSmIyaKmEj6wr71vlsX15Mbai+VOzQwjBAnZtLQlMKyo82jNXUJskOFKJAvJdIbxugbQ52YSbHoY1pR/cYgCPG8gDCYWMdW0wLPC/ZPsSnl8JvDnVZNiyAIotX74QozlkkYgO1YZJrTxGJRo6R0TYx4yiYMQwoFv9TgKWBooMirL3aQSDqElGrKByHnvGc5dQ0p8jmX3duG2P1WN7G4TbHgQRhd2JiWiWVFq+5+qXOrE7MIvJCaUi3/4RrwwxdZv37o9Wgza+n2g2maBAQ89+QOzr/o2DE7pSaSDvWNVaTScTzXZ/eO/nKev1v0Z3RDqbo0ioiIyGQ7YoL5idg3HSKRdFhyTIZtr3TjxCxSVTFCIyQMDBqbqzBLFVaOeUcTm5/bw3CL0zEbw5ZW8pOlspQdu0tVdUrB91j574YBiZRFWAqSwxAMAnw/es52zChIDEMsKzr/YH8R1/VHnNM0wXEsqmvjGIZBf2+e6tpE+U0MiC42gKCU6+8WAyzboJB3SZgxmlrSmJbJnp39JJIO27Z20bYzixOLAvP2XQPlDcOBHwX0yXSMRMIurcAnOfWshezZ2T/qZs/soFsO5N8et0l20D1op9ThPRK2Y3HUwhp6Sw2/ausTU16BZayLDJh7XRoP9FlFRERkeowrmM/lciSTyf0e7+zspLGxcdIHNdNG2zCbG3KZ11pNU0s1AK4b1aof6i+waHlDeXV1aMBl9/Y+ervzY54/CCBZZZNIOVimSU1DitxggWIxwC34b+fkG1GFmChXP6rX7vtQUxcnDEJyWRffM8GIjonF7WjTqmmQrovj2AbZIReK+79/Kh2LPlfWJTtYJFXlkM+5BF5ILG5FOf/75O77peZQiaRNb3eOxuY0A335cmCdGypSyHn4fkB1bZyB/kLUKTaI7kakqhyOWliLZZnloHqsHPhU2iGXLY4I6IMg6hZ7sE6pe6+A26Os+k+Vg11kwNzZ4DqezyoiIiJTb1w58zfccAOu6454bM+ePXzhC1+YkkHNtNHqfXtFn3RNnM49g7Tt7KevO0ddJsW81ppyM6JE0mH12mW0LqmjuTU9Zt34MAypqo42ky47vpGammhDaRiEWI75dqpNCBgBhgGWE1WWSVU5tC6uo3VJHQuPriOVdojFLOIJp5TLH5abJbludKxpRbXXh8djmjDQn2egrxClwxhgORbPbtyB73v09xTKr9tXVMM9qtwTBCGD/YVyQGc7UVlJyzKpqonTsqCG6toE9U1JFi2r5+hjGmlqSY8r4Dv1rIWEQRTAR99BlIZ06lkLD7qRdKZqoo92kWGYBtu2dk3p+86EQ/2sB6qzLyIiIhM3rpX55cuX8/d///dce+21WJbFrl27+PKXv8wHP/jBqR7fjBgtHSIWN3h9SzemEVVd8Vyfgf48maYUv/nPnZh2tJwe+OAWfOYvqqOvJ0cx7++3wh3lght4RZ/FKzLseL27lDYTYhgmpmW8vVEyMKiuixOLmxQLAQ1NVTS1pGleUMOO17vp783juQGe50d13wmJOya5wSKxuEUYGFhWFJXbjo3n+oRhSOCH4Ly9AXOgN09NXYLuzkJ5fP4+mzVNK0rPCcOodGQYhKRrEuWxZpqq6OsdjDKFApg3v5rGeeML3vdV15Bi7QeO47knd5AddEmlnXI1mz3J/oOWGp2JFfAjqVrNoXxWreaLiIhMvnGtzH/84x+noaGBr33ta2zfvp3//b//N5deeikXXnjhVI9vRoyWC+zEnRF5574fkB0okh1yGRoosOU3e9jyQjuDAwWKrs+u7X1R2ssoMxxiYMcslh7fGKWaZFI4MatUKSeq/DIsCKGQc8kOujgxEydmMtCX57F/f4U3XunCMKIOrk7MJpGyqUrHMIBk2sGOWRgWI3LXnZiF7VjEEjaWZVJTm8CyLPJZlz1vDZAbdFm8PINtR6UzLduIVvdtg9qGJIlkVHVm0fIGVq5eSHVtvBzMOzGblgU1xJM2yeThr4jXNaQ4/6Jjed+l7+D8i44tp+TM1k6pR1K1mkP5rEfSnQsREZHpMu7SlFdccQWmafL5z3+eP/qjP+K8886bynHNmH3LUvZ05Xh24w4KWY/5i2qjPHc7KgdZXRuH0KC3O6ppbhpGOf0GI8pxN03z7fQWC2zHoCodK3eQeupXb7LjjZ7Saw1icatcntI0o3/CMColWci5UU7+jn662rN0t2dLAZRDPGGVykRmSFbF8YoB+axLMulgYBDutSG3ujZOy4I0qSoH245SY4ZTdDzPJxa3WX7iPFLpGKl0jHRtnNr6BFVVMVa8o5FlJzQReCHbtnbRvKBmRGBtWSYNjVWsee+KcvrRZJupNJqDma0XGVPhUD7rkXTnQkREZLqMuYx2/fXXl+upD/M8j3g8zoMPPsiDDz4IwBe/+MWpHeE0G2tz5WB/nkQqRmNzGoC2nf14ro/tRBtUgzCkkPPIDkW7TZta0mQHC3jFUgqMZRL4UYOo2voEnhvQ252nfdcAgR/gFqM0m2LBx3OjblOhQTkX3i16uMVoZX344gGiAClVFSsdF9CywCiNyY86q9Y5pNIOPaUmSq1H13H6OYt56bnddOzujMptmgZhENW7r29K4BY9YnGbpcc20tOVxSv6LD2+kXnzq9n02HZ8LxpHTV2C7o4sx69sYc/OfizTwYknp6WqyUTTaKaj8spcq1ZzIIfyWQ+3E7OIiIjsb8y/RS+44ILpHMesMdbqYbomjlsMyjXCLdugWAipa0jS1THEYF8BCKMOo1mX7FCRY0+ax4KlDbyxpZOh/ijIT9fGCcOQeNKitzNLbqhYrr/ue1GH2WhVP8o7z+eK2I6N7wdYtkVtQ5Le7lxUlrLUMRUg8MOobj1Q25Akl3XLuestC+ponl9bXr3O51y62rPEkzaFnEduqIhhRLn5phk1s6qqiRF4IdV1cTAMClmPhx94BdMycBybocEi3e1DVFXHKORdzrxgKQsWHkVnZ+f0fVnjNJ252nOlWs14TPSzqs6+iIjI5BszmD///POncRizx1irh7X10Yrz8ErkomX19HblygF0OS89CBkcKGAQpcYsPbaRLc/tIRa3ME2TYt5jqL9A69F1vPVmH0EQUCz4USfYEBJVMTzXx/cCDDOqTxn4UcOnVJVJ154hIAqCMENMK2rwZJiQro4CU8exaFlQQ29XNkpjyBZJ18TZtrWr/BnSNXF6OobIDbkEQYhpRXcF6jMpTMsk5lgsPiFTDoK7O4bI51y8YoBpQ7EQXXgUiz5hCM9u3EHm/Q2HNfdTtXp+sFKWMj2OpDsXIiIi02Vc97fvvPNOzj77bI499tjyY1u2bOHJJ59k3bp1UzW2GXGg1cN9VyLzOZetm9vZtrWI7VgU8sVyicZ4wmb7q1GTqeaFNQz05ikWPLxigGEZ7HyjF9MycAtRqZuwVPEmn43OEYbRRljLNIgnbDw3ymfv78nh+QFF1yMWs6iqjuHELNK1MfJZj907+sopMNW1CQwj2pgaBNDTlaO7YweY0LF7AM+L0oPCMMT3jdKdgWij7PZXu9n+WjfFYhTgF/NeVDXH9wmK0V5g04BiwSU7ZOH5Aa9sbqP16KpDmvepXD1XrvbscSTduRAREZkO49oA+8QTT7Bs2bIRjy1dupTHH398SgY1kya6uXKwt0C6Nh51YLVtwjBa3TdNAydm0batj3jcpq4hie9FaTjJqhjFoodb9An8sJQvH210DXzw3KiDq1XaVGvHLFI1DqZpRSk4pkkiYUOpQVQ8YeE4FvVNVTgxC7cYNbRK18ZxYvZ+1UPadvSRz7nlC49o8210V6GrY4hd2/souj65rEch57F7Rz/5nItlm4RhdKFhGkZpQ61BvFTaMjdUHHWORrNvvfGtm9unrNLJkVRlRkRERI4s44pmDMMoN+8ZFgRBObVktsnnXDY/+xbbX3ueQq5IpqWKk06fT29nbly398e7ejicvlGXSZXSX8Awoq6qyaoYdZkU2YECQRDS253DMCAIQ7KDRcIAip6P7+0/h6ZFuUpNVXUM1w3wCgGWbVKVjhMEAQN9AaZJ6Y6ATy6b56iFteUNukEQ0rV7kNrMyA6rphnVnQ+D6OLBtk3ypdrzvhfQ2TZIVXWcukyKvu4cnht1l/WDsNRdllIzq+j3RNLBtqILiGRVbNzfz76r8G07+5h3VHV0wbLXWCdj9Vy52mObjo3BIiIiMnXGtTJ/3HHH8f3vf39EN8777ruP4447bkoHdyjyOZeNG17jN/+1i/6eXDlY+en3X2T3zv4R5SYPt/vkcPqG41jUNSaxS6UoTcukZUENlmXSsjAq3VjIuQz2F2jfNcBAb6G04XWvQH6vLJDAj3LwTaNUMjKMNsgmU075fYFyJ1bfC8tlMYf5fkBfb47dO/ro3DOIW6qQEwQhNXUJ6jKJ0nijrq6mCdZwKkrpX7UNyXJpfdMwopQeJ6pn7zgWiaSN45gEQYBlGRxzwvjSJ0bLYXdiFj1d2RGvm6zV89laynKmjVWGVV1ZRUREKse4IqVPfOIT3HDDDVxxxRU0NjbS2dlJfX0911577VSPb8K2be2iY3fUhdQ0DXzfIAzAD33a3+pnyTGNk7YB0rSgo20A34tWqWNJG8s0SFbFSqvfIStObCafc3ljawdDA4Vybnzg73MuAzAgCKLa8umaKN89O1hkXmsVxXyAW4wO8v0ADIglLGwnuh7z3Kg0JYDr+uze0UcsYeMWAooFj+6OIVJVMWIJm1XnLuKVF9opFgLyOZdYzAJsUtUxEkkbt+DT152jsTld3kibrHLwvYCmljRd7VmCIKCQ90iWznnOu5eRrIozlBs46LyNlsNel0nRuXsg2ow7BavnytXenzYGi4iIVL5xBfOZTIYbb7yRV199la6uLjKZDMuXL8c0x91zatrkcx6u62Ps1Xo1DMHAwC2+nSp0uCkc+ZxLb1eO3NDblW98L8ALAmJxm3y2yKlnRau/27Z2kaqK0deV3+88pgkY0So7BlHAbhqkqpxSelNI65IGFh5dz+O/eA3fC6J8edPANE1qG5IYwK7BIpYdjaO3KwshNM5L43nRZlY/CHBdn6b51bz+UhcnrWqlrrGHzc/sxrRM0jUxMvPSGMDuHf3lC4fhJlArVy8EKFXCSTDYnyddE6e6NjHh1IzRKgZZlsnS4xuJxWylfEwTbQwWERGpfOPOYTBNk2OOOWYqxzIpotQPC88tMpxFNJyr7sTeDvAPN4Vj29YunJjN/EW19HbnKORcPNentiFJc2sNQRDy0rNtHL+yhe2vdtPVnmW0LQZBAE482oQa+AG2bZCuSeDEbGzHpKo6Rtv2PgIvZPHy+nLN97adfdRmUjiORRCENLdWU5dJEvhRQBZP2nTuGSKfLRJPOdiWiWWbxOPRZtA9O/s56Z2txByLnq7ciKCuubUaz/WJJ+z9gurJWLEdK4d9xYnNCt6nkZo4iYiIVL5x/a2dzWa577772Lx5MwMDAyM2vv7zP//zlA3uUCxekeGtbb1se627VMEkqsFuGxbzWmuA8bWeP5jhVU3TtGhsTtO5ZzAK1sO3UxaKrs/jD75KEEIx740azJsmxBMWtXVJCnkXwzRpXVKH41jldJlkyqGQ98rjXrl6IctPbOK5J3fQ2ZMnlXY49ayF1DWkyOdcdrzeTT7rUiz45Qoz1XUJqpOJ8tiGV19HC6wty+T0NYunLLBWvfHZQRuDRUREKt+4gvlvf/vbdHd386EPfYhbbrmFq6++mh//+MecccYZUz2+CUskHVavXUZdJsH21/pL1WzqJlTNZnzvM3JVczhffTiH3XV93trWi1vwwYRgjMI/dU1J3vHOVgIvxLQNejqyWKVGVMPpMvWlijTDOc1bN7cz2FsgkYqRSsfLdwGGA+R0TZyOPUMYhGAYeK5Pf0+elgVvX8wMr77OVGCtHPaZp4sqERGRyjeuYP7555/n5ptvprq6GtM0WbVqFcuWLePGG2/k/e9//1SPccISSYfTVi/h3f+/aLPusOb5tZP2Hvuualq2QbEQUteQxHV92nb2l2q5m+QGSl2W9mKaYMcslizPcNI7W8uP710qMOZY1C9KYTsjyzW2beujuj456sbFfM4jO+SSro5RzHuYZlRC1HEMhgaKxBPOfquvCqyPXPruRUREKtu4gvkwDEmlotXhRCLB0NAQdXV1tLW1TengpsOh1tned1Vz0bJ6ertymJZJd8cQgR/gewFF3y91c6WUhhMdb5Q6u3a1Z8nn3PJ77h1cbXm+jZ6u3Ij3DYIQDMbcuJhI2rhFH9u2sNPRRUDK9/H9EM/1yWeLpGsSbNvapVVYERERkQo3rmB+8eLFbN68mZNOOonjjjuOf/mXfyGRSHDUUUdN9fim1GjNi7o7doy7Bvm+q5rDFwYduwco5DyqquP09+QpR/BR1guWHVWiSaYc6hqS5cB634uKsXKaWxbWMDQw+sbFxSsybN3cgedGdwXCMMQwTeY1pxgaKJJIxQiCkJ6u3IQ+q+xPDZdERERkpo2rtuQVV1xBU1MTAJdffjmO4zA0NMRVV101pYObaqPV2TZMg21buw7pfMPBfXVtnGSVQywWNViyLBPLMjDtqHKNbVtU18ZpXVxHLG4z0FcYtXkPMGqzoxUnNhMGYWmD78gNvYmkwznvXkY8YWNaBomUw7z51Qz1F6hrSB7yZ83nXLY838Zv/nMnW55vO+IbC6nhkoiIiMwG41qZb25uLv9cU1PDJz/5ySkb0HSaqjrb6Zo4XR1ZwjAkWRXD8wJsxyrXgQ8DqKqJA1Egnh2MNrOO1bxntJzmA21crGtIsfaS40c8n0w65eB/op91rDsYx69sYc/O/vJ7VJ1RfTjTVlHUcElERERmg3EXlH7ooYd44okn6Onpob6+nrPPPpt3vetdGIZx8INnqamos53PuQz2FzBKDaBiMStq3uR6DA5EZSJTaQe34LNrex/NrdWka+IEwcjzHCzQPtjGxX2fH86/P5TPOlrgWnB9Hv/FazQ2pzFNg4G+PPd/dxMN8xKH1Eiq0qjhkoiIiMwG44pav/vd77Jp0yYuuugiGhujCjEPPPAAu3bt4mMf+9hUj3HKTHad7eEVbNuxCAOwbZMghLrGFH1dWY4+pgbDNOjrzuG5AZZtUJdJEovZhxxoj9fhfNbRAtfeziwDfXkCP6rjn8+6xOMx2nd7eF445/Px1XBJREREZoNxRR6PPPIIN954I5nM24HfaaedxrXXXlvRwfxk19neurmd7s4h8jmPgf48vhdgmlBV7dCyoLac5tLYnMZzfXq7c+x4rZeFy+pwix5OzJ6y5j2H81n3DVxd16e7cwjLMvG9gKHBAp4bEIvF8NzgiEg5UcMlERERmQ3GFcwnk0mSyeR+jw2Xq6xkk1VnO59zeW1zB8W8R093rly5xoxZ7Nk5wLz51Xj5ENOMmjjt3tEPRDn1vd15utsHiSdiWJZBy+JaFh5dP2rgfTgVVA71s+4buPZ2ZTFNs7wKHQbRZ80OFaht2L/L7FSbiaoyargkIiIis8GYwfyePXvKP7/vfe/jH/7hH7jkkktoaGigq6uLH//4x1x00UXTMshKsG1rF07Moqt9CAOi6JYQzwtIJB2624eoSicIgN7uHMPRflV1jPZdAxCGOGFIXX2K3s4sPR1ZYvFopb6/L8/WF9vJtKTpahukNpMiHrfHXUrzcIPdfQPXmGOxaFk9nXuGCMMQ0zLw/RDfD6ltiC76pivl5HDLix4ONVwSERGRmTZmtHXNNdfs99iLL7444vff/va3/Lf/9t8mf1QVKJ/zqMuk2LWtF98rLVWHIZYd1ZMv5gPOeXcUEHe3D5GsilHbkKSvO4cBGKZZTlHp780D0NRSjev65WB/x6vdWLZJ+64BWhbU4DjWQdNZJivY3beZVUfbIE7MZLC/CEaIYRpk5qWjMU1jyomqyoiIiMiRbMxg/gc/+MF0jqPiJZI2/X35tyv3hxAt0YcEYUgq7YwIiIc3vHpugGEYhGGI7UQH+97bJST3Dvbzpbz6MAzp686VK8kMp7OMtgI/FcFu84IaXti0C8OkVPIywLcDlh7TRD6fn9aUE1WVERERkSPZhPMgXn75ZY477ripGEtFW7wiw9YX26muSdDfnSckxDBMbMekkHU59ayFI147nINuOyZu0QMM6kopKsP16IERwb4TK3V0NaKLAHg7nWWsFXgnZk16sLtnZz/NC2sY6M3juQFxx6a6LkG6JsmxpzQe8nkPharKiIiIyJFsXB1g9/bVr351KsZR8RLJqGJNTV2STHMViaRDsipGVXWc5SfOo64hNeK1w51d5x2VJp50aG6txi6lqKSqHDw3YPeOPnLZIq7rEYbQsrCWMIQgCLAdc0Q6y1jdbAf78/s1izr8Wvoe8bhNY3OalgU1NDanoxz+oeIhn/NQLV6RGbMbroiIiMhcN+GILgzDg7/oCFVdG8fzAszmdPmxIAipz0Qr7mNtRN37cdMCt+iRmVdFf2+ewA8YHCiw8OhqEqWgv7c7x7yj0iOaM42VbpKuieMWg0ktoTi8Gu77wYia+U3NdYd8zkMfi6rKiIiIyJFrwsF8U1PTVIxjTjhQ7fGDbUTde3PpcL35xtJFQbHg4bk+8YRNbX2C09cs3i9YHSvdpLY+WV65n6xgd/GKDHt2vUn77gHMUgfgYiGkq32QoxZXTXsgraoyIiIicqSacDB/0003TcU45oQDrRJveb5tXBtRx1phdwv+Ad+7eUENW19sx/dDnJhFdV0C2zLL7z+ZwW4i6VDflGKgL4/vRRt36xqSxOKOqsiIiIiITKMxg/mHHnpoXCe44IILJm0wc8FYgfN4q67su8LuuT67tveRrHIo5L1RS0vmcy4vPdtGXSZFf28et+jT153jnHcvm7JV8sALaWqpHuXzuFPyfiIiIiKyvzGD+V/96lfjOoGC+fEZb9WVfVN1erqyYEBdJtpAO9qK/vDm15htl1NzgiBkz87+ERtvZ+LziIiIiMjUGTPy+sIXvjCd45jzDpRPv7d9U3Us0yQWt+jaM1ROZ7Eda8SK/kzUWh/t8wSHsbH2cLvUioiIiByJxgzmh+uZQ1QKcSymOeHqlkekiVRdGU7VyedcdrzejVvwMU0Tz/XZPeTS3FpNbX1ir9dP/yr5aJ/ntDNWMJQbmPC5hjcHe37AQClNaOuL7ZzznuVTdmdBREREZC4YM9pbt24dd999NwAf/ehHxzyBOsWO30Q3om7b2kVtJkX7roHyxVUYBvR25zh9zeLy68a76j/Z9v08yar4IQXz27Z24fkB7bsGom63hoHn+jz+i9dYe/FxWqEXERERGcOYwfzeVWtuvfXWaRnMkeZgqSXDzZlaFtSU67nHHZt5R6VHvK7Sa63ncx4DvflyIA/RHR/fC1QdR0REROQAxgzmGxsbyz+rtvzkO1jdeXg7fcZxrBEbW6trE+VzVGoAv7dE0sYt+uVAHqI0LydmT2nev4iIiEilG3dS9VNPPcXmzZvp7+8f8fhVV1016YM6EgxXoDlQ3fnDaUJVSRavyLD1xXY8N9obEIYhYQg1dQlVxxERERE5gHHtXr3vvvv45je/SRAE/PrXvyadTvOb3/yGVGpyNicGQcD//J//kxtuuAGAwcFBvvSlL3HNNdfwpS99icHBwUl5n9lkPBVohtNn6jNJ4gmb+kyyHKyPdjFgmAbbtnZN6+eYDImkwznvWU486WBaBomUQ3NrNVap6ZWIiIiIjG5cy54PP/wwf/3Xf82iRYt45JFHWLduHeeccw7/7//9v0kZxM9+9jNaW1vJ5XIArF+/npNOOolLLrmE9evXs379ej72sY9NynvNFuOtQHO4TagqRV1DirUXHzcn0oZEREREpsu4VuaHhoZYtGgRALZt43key5cvZ/PmzYc9gK6uLp555hkuvPDC8mObNm3ivPPOA+C8885j06ZNh/0+s83iFRnCUm12YMIVaBJJu3zssEpv2jR84XLKGQs49uQWBfIiIiIiBzGuyK+lpYUdO3awcOFCFi5cyC9+8QvS6TTpdPqwB3DXXXfxsY99rLwqD9DX10d9fT0A9fX1++XpD9uwYQMbNmwA4IYbbhixaReiC499H5tNMu9v4JXNbeSGiiSrYhxzQgvJqvi4jq06o5onHtqKWUq1GW7adNoZK8Z9jsk22+d7LtKcTy/N9/TSfE8/zfn00nxPv7k45+MK5i+99FIGBqL64Zdddhlf//rXyefz/Mmf/MlhvfnTTz9NbW0tS5cu5cUXX5zw8WvXrmXt2rXl3zs7O0c839jYuN9js03r0VVAFQBDuYEJ1Wk/7tRMKS3FLaelTPQck6kS5nuu0ZxPL8339NJ8Tz/N+fTSfE+/Sp3z+fPnj/ncuIL50047rfzzihUruOWWWw5/VMCWLVt46qmnePbZZykWi+RyOb7+9a9TW1tLT08P9fX19PT0UFNTMynvN9dMtAmViIiIiMwtBwzmx3Plcji3Ki677DIuu+wyAF588UUeeOABrrnmGu655x4effRRLrnkEh599FFWrVp1yO8xGeZKPXcRERERmVsOGMxfeeWVBz3BD37wg0kbzLBLLrmEm2++mYceeojGxkY++9nPTvp7jNdM1XPXBYSIiIiIHMwBg/lFixbhui7nnXcea9asoaGhYcoGcuKJJ3LiiScCUF1dzfXXXz9l7zUR42nuNNnmUkMoEREREZk6Bwzm//7v/57t27fz6KOPcv3119Pa2sq5557LGWecQSwWm64xzqiZqOc+VRcQWu0XERERmVsOWmd+0aJFfPzjH+fWW2/loosu4umnn+bP/uzPeP3116djfDNuJuq5T8UFxPBqf09XjkLeo6crx7Mbd5DPuWO+fsvzbfzmP3ey5fm2MV8nIiIiIjNnXE2jANra2ti8eTNbt27l6KOPnpQa85XgcJs7HYqpuIAYbbXfMA22be3a77UTDfxFREREZGYcMDocHBzk8ccf59FHHyWfz7NmzRq++MUvzrli+weSSDqsXL1wWtNTFq/I0N2xgwDKDaEO9wJiIqv9M7FPQEREREQm7oDB/BVXXMG8efNYs2YNxxxzDBCt0Le1tZVf8453vGNqRzgLTHc996m4gEgkbXJZd0RAP9Zq/0zsExARERGRiTtgMF9XV0exWOSXv/wlv/zlL/d73jAMbr311ikb3JFssi8gJrLaP5HAX0RERERmzgGjs9tuu226xiFTbCKr/VOR5iMiIiIik09LrRNUyeUdx7vaPxP7BERERERk4hTMT8CR1MxpuvcJiIiIiMjEKZifgMmo8nKoK/uVfEdARERERKaGgvkJONwqL4e6sn8k3REQERERkfEbd9MoOfxmThNp3DQZx4mIiIjI3KZgfgIOtxvsoa7sq+67iIiIiIxmTqbZDOeXv2r24AeFScsvH6vKC8CW59sOms9+qPXbVfddREREREYz56LBvfPL02mTwcHcpOaX71vlZSL57Idav11130VERERkNHMuzWa688sn8n7DK/v1mSTxhE19Jjmui4xDPU5ERERE5rY5uDI/vfnlE32/Q63frrrvIiIiIrKvObcyf7gVZ2b7+4mIiIiIDJtzEefe+eUw8Yozh/N+B8pnV9MnEREREZlscy6Y37vijGU6OPHklAbOY1W42fv91PRJRERERKbCnAvm4e388sbGRjo7O6ft/cYy2ibZoPS48uBFRERE5FDNuZz52UhNn0RERERkKszJlfmZtm9+vGlFuftq+iQiIiIik0nR5CQbLT/eLXqEIcTitpo+iYiIiMikUTA/yUbLj3diNlU1MWKOpWo2IiIiIjJpFMxPsrHy4wMv5Nh3arOriIiIiEwebYCdZGoiJSIiIiLTRcH8JFu8IkMYhOWAXvnxIiIiIjJV5uRy8XA1mVfNHvygMOX56ftWrzl+ZQt7dvYrP15EREREptScC+b3riaTTpsMDuamtNvq6N1ds+ruKiIiIiJTbs6l2YxWTcYwDbZt7ZoT7yciIiIiMmzOBfPT3W1V3V1FREREZKbMuWB+uqvJqHqNiIiIiMyUORfMT3c1GVWvEREREZGZMueWjxNJh5WrF7JtaxeW6eDEk1NaTWbv91P1GhERERGZTnMumIcowD725BYaGxvp7OyctvcTEREREZlOcy7NRkRERETkSKFgXkRERESkQimYFxERERGpUArmRUREREQq1JzcADuX5XOuKueIiIiICKBgvqLkcy7PbtyBYRqYpkEu69LdsYOVqxcqoBcRERE5AinNpoJs29pVDuQBTNPAMA22be2a4ZGJiIiIyExQMF9B8jmvHMgPM02DfM6boRGJiIiIyExSms0sNXpuvE0u644I6IMgJJHU1ygiIiJyJFIUOAuNlRt//MoWujuyBEQr8kEQEgYhi1dkZnrIIiIiIjIDFMzPQqPlxgfAnp39rFy9UNVsRERERARQMD8rHSg3PpF0OPbklhkamYiIiIjMJtoAOwslkjZBEI54TLnxIiIiIrIvBfOz0OIVGcIgLAf0yo0XERERkdFoqXcWSiQd5caLiIiIyEEpmJ+llBsvIiIiIgejNBsRERERkQqlYF5EREREpEIpmBcRERERqVAzmjPf2dnJbbfdRm9vL4ZhsHbtWt73vvcxODjIzTffTEdHB01NTXzmM58hnU7P5FBFRERERGadGQ3mLcvi4x//OEuXLiWXy3Hddddx8skn88gjj3DSSSdxySWXsH79etavX8/HPvaxmRyqiIiIiMisM6NpNvX19SxduhSAZDJJa2sr3d3dbNq0ifPOOw+A8847j02bNs3kMEVEREREZqVZU5qyvb2dN954g+XLl9PX10d9fT0QBfz9/f2jHrNhwwY2bNgAwA033EBjY+OI523b3u8xmTqa7+mnOZ9emu/ppfmefprz6aX5nn5zcc5nRTCfz+e56aabWLduHalUatzHrV27lrVr15Z/7+zsHPF8Y2Pjfo/J1NF8Tz/N+fTSfE8vzff005xPL8339KvUOZ8/f/6Yz814NRvP87jppptYs2YNZ5xxBgC1tbX09PQA0NPTQ01NzUwOUURERERkVprRYD4MQ26//XZaW1t5//vfX3789NNP59FHHwXg0UcfZdWqVTM1RBERERGRWWtG02y2bNnCY489xqJFi/jLv/xLAD760Y9yySWXcPPNN/PQQw/R2NjIZz/72ZkcpoiIiIjIrDSjwfxxxx3HD3/4w1Gfu/7666d5NCIiIiIilWXGc+ZFREREROTQKJgXEREREalQCuZFRERERCqUgnkRERERkQqlYF5EREREpEIpmBcRERERqVAzWppytsjnXLZt7SKf80gkbRavyJBIOjM9LBERERGRAzrig/l8zuXZjTswTAPTNMhlXbo7drBy9UIF9CIiIiIyqx3xaTbbtnaVA3kA0zQwTINtW7tmeGQiIiIiIgd2xAfz+ZxXDuSHmaZBPufN0IhERERERMbniA/mE0mbIAhHPBYEIYnkEZ+BJCIiIiKz3BEfzC9ekSEMwnJAHwQhYRCyeEVmhkcmIiIiInJgR/zycyLpsHL1QlWzEREREZGKc8QH8xAF9Mee3DLTwxARERERmZAjPs1GRERERKRSKZgXEREREalQCuZFRERERCqUgnkRERERkQqlYF5EREREpEIpmBcRERERqVAK5kVEREREKpSCeRERERGRCqVgXkRERESkQimYFxERERGpUArmRUREREQqlIJ5EREREZEKpWBeRERERKRCKZgXEREREalQCuZFRERERCqUgnkRERERkQqlYF5EREREpEIpmBcRERERqVAK5kVEREREKpSCeRERERGRCqVgXkRERESkQimYFxERERGpUArmRUREREQqlIJ5EREREZEKpWBeRERERKRCKZgXEREREalQCuZFRERERCqUgnkRERERkQqlYF5EREREpEIpmBcRERERqVAK5kVEREREKpSCeRERERGRCqVgXkRERESkQimYFxERERGpUArmRUREREQqlIJ5EREREZEKpWBeRERERKRCKZgXEREREalQCuZFRERERCqUgnkRERERkQplz/QAJtvQK2/y1l0/ovfJ57Asi+ozT6F13QeoWr54pocmIiIiIjKpZnUw/9xzz/Gd73yHIAi48MILueSSSw74+s5fPMHmq7+EYRg0nP87xByHtv//g7Td93NO/Oe/JXPBmdMzcBERERGRaTBrg/kgCPiXf/kX/vqv/5pMJsNf/dVfcfrpp7NgwYIxj3np0/+H9PHLaP7Ktfx6UwdD/R7WgjNZ+PPvsfmar3DWxnuxa9IjjsnnXLZt7SKf80gkbRavyJBIOvude7yvOxRTeW4RERERmbtmbc78q6++SktLC83Nzdi2zerVq9m0adMBjwlyeTJ/cQUP/nwnHbuHKOQ9BsMYr5/2HoKhLDvve3DE6/M5l2c37qCnK0ch79HTlePZjTvI59xDet2hmMpzi4iIiMjcNmuD+e7ubjKZTPn3TCZDd3f3AY+JNWd45g0fz/cxTQPDAMMwKTQdhZ+son3TyyNev21rF4ZpYJoGQHSMabBta9chve5QTOW5RURERGRum7VpNmEY7veYYRgjft+wYQMbNmwA4IYbbsAfGMIdLGCZVvl40zQwiy5GsYCVrKKxsbF8/KtmD+n0/tczlukc0usOxVSee7rZtl1xY650mvPppfmeXprv6ac5n16a7+k3F+d81gbzmUyGrq63V6e7urqor68f8Zq1a9eydu3a8u9+Nk/1b56kd9npEIJpmgRBSO3zv8b0PWJnr6Kzs/Pt1wcFBgdz5VVxgCAIceLJQ3rdoZjKc0+3xsbGihtzpdOcTy/N9/TSfE8/zfn00nxPv0qd8/nz54/53KxNs1m2bBm7d++mvb0dz/PYuHEjp59++gGPabjgDFL/8VOan/g5sT1vEdvzFvN+9RPm/ddDBO84mRUX/c6I1y9ekSEMQoIgWsUPgpAwCFm8InNIrzsUU3luEREREZnbZu3KvGVZXH755XzlK18hCALe9a53sXDhwgMec8Kt1/Pq394C/+8/qHv+1wCEpoV13hpW3fy5/SrEJJIOK1cvPGglmfG+7lBM5blFREREZG4zwtGS0yvUrl27ACi0d9H/1G+prqnBPHYxsaaGGR7ZkaFSb11VMs359NJ8Ty/N9/TTnE8vzff0q9Q5P1CazaxdmT8c8XkZmt53XsV+YSIiIiIi4zFrc+ZFREREROTAFMyLiIiIiFQoBfMiIiIiIhVKwbyIiIiISIVSMC8iIiIiUqEUzIuIiIiIVCgF8yIiIiIiFUrBvIiIiIhIhVIwLyIiIiJSoRTMi4iIiIhUKCMMw3CmByEiIiIiIhM3p1fmr7vuupkewhFF8z39NOfTS/M9vTTf009zPr0039NvLs75nA7mRURERETmMgXzIiIiIiIVak4H82vXrp3pIRxRNN/TT3M+vTTf00vzPf0059NL8z395uKcawOsiIiIiEiFmtMr8yIiIiIic5mCeRERERGRCmXP9ACmwnPPPcd3vvMdgiDgwgsv5JJLLpnpIVWkzs5ObrvtNnp7ezEMg7Vr1/K+972PwcFBbr75Zjo6OmhqauIzn/kM6XQagPvvv5+HHnoI0zT5xCc+wamnngrA66+/zm233UaxWGTlypV84hOfwDCMGfx0s1cQBFx33XU0NDRw3XXXab6n2NDQELfffjs7duzAMAw++clPMn/+fM35FPnJT37CQw89hGEYLFy4kE996lMUi0XN9yT6xje+wTPPPENtbS033XQTwKT+f8R1XW699VZef/11qqur+fSnP828efNm6uPOuNHm+5577uHpp5/Gtm2am5v51Kc+RVVVFaD5ngyjzfmwH//4x3z3u9/l29/+NjU1NcARMOfhHOP7fnjVVVeFbW1toeu64ec+97lwx44dMz2sitTd3R2+9tprYRiGYTabDa+55ppwx44d4T333BPef//9YRiG4f333x/ec889YRiG4Y4dO8LPfe5zYbFYDPfs2RNeddVVoe/7YRiG4XXXXRdu2bIlDIIg/MpXvhI+88wzM/KZKsEDDzwQ/tM//VP41a9+NQzDUPM9xW655ZZww4YNYRiGoeu64eDgoOZ8inR1dYWf+tSnwkKhEIZhGN50003hww8/rPmeZC+++GL42muvhZ/97GfLj03mHP/85z8P77jjjjAMw/Dxxx8P//Ef/3EaP93sM9p8P/fcc6HneWEYRnOv+Z5co815GIZhR0dH+OUvfzn85Cc/Gfb19YVheGTM+ZxLs3n11VdpaWmhubkZ27ZZvXo1mzZtmulhVaT6+nqWLl0KQDKZpLW1le7ubjZt2sR5550HwHnnnVee302bNrF69Wocx2HevHm0tLTw6quv0tPTQy6X45hjjsEwDM4991x9J2Po6urimWee4cILLyw/pvmeOtlslpdeeokLLrgAANu2qaqq0pxPoSAIKBaL+L5PsVikvr5e8z3JTjjhhPKq+7DJnOOnnnqK888/H4AzzzyT3/72t4RHcC2N0eb7lFNOwbIsAI455hi6u7sBzfdkGW3OAe6++27+8A//cMRduiNhzudcmk13dzeZTKb8eyaTYevWrTM4ormhvb2dN954g+XLl9PX10d9fT0QBfz9/f1ANPcrVqwoH9PQ0EB3dzeWZe33nQz/j01Guuuuu/jYxz5GLpcrP6b5njrt7e3U1NTwjW98g23btrF06VLWrVunOZ8iDQ0N/N7v/R6f/OQnicVinHLKKZxyyima72kwmXO899+zlmWRSqUYGBgopzTISA899BCrV68GNN9T6amnnqKhoYElS5aMePxImPM5tzI/2pWT8igPTz6f56abbmLdunWkUqkxXzfWVetsvpqdTZ5++mlqa2vLd0MORvN9+Hzf54033uDd7343f/d3f0c8Hmf9+vVjvl5zfngGBwfZtGkTt912G3fccQf5fJ7HHntszNdrvqfeocyx/p4dvx/96EdYlsWaNWsAzfdUKRQK/OhHP+LSSy/d77kjYc7nXDCfyWTo6uoq/97V1VVejZCJ8zyPm266iTVr1nDGGWcAUFtbS09PDwA9PT3lK9V95767u5uGhoZRv5OGhoZp/BSVYcuWLTz11FNceeWV/NM//RO//e1v+frXv675nkKZTIZMJlNetTnzzDN54403NOdT5IUXXmDevHnU1NRg2zZnnHEGr7zyiuZ7GkzmHO/9nO/7ZLPZUVMejnSPPPIITz/9NNdcc005ENR8T409e/bQ3t7OX/7lX3LllVfS1dXFtddeS29v7xEx53MumF+2bBm7d++mvb0dz/PYuHEjp59++kwPqyKFYcjtt99Oa2sr73//+8uPn3766Tz66KMAPProo6xatar8+MaNG3Fdl/b2dnbv3s3y5cupr68nmUzyyiuvEIYhjz32mL6TUVx22WXcfvvt3HbbbXz605/mHe94B9dcc43mewrV1dWRyWTYtWsXEAWbCxYs0JxPkcbGRrZu3UqhUCAMQ1544QVaW1s139NgMuf4ne98J4888ggAv/71rznxxBNn9arlTHjuuef4t3/7N6699lri8Xj5cc331Fi0aBHf/va3ue2227jtttvIZDLceOON1NXVHRFzPic7wD7zzDPcfffdBEHAu971Ln7/939/podUkV5++WWuv/56Fi1aVP5D/NGPfpQVK1Zw880309nZSWNjI5/97GfLV6w/+tGPePjhhzFNk3Xr1rFy5UoAXnvtNb7xjW9QLBY59dRTufzyy2f1fxgz7cUXX+SBBx7guuuuY2BgQPM9hd58801uv/12PM9j3rx5fOpTnyIMQ835FPnhD3/Ixo0bsSyLJUuW8D/+x/8gn89rvifRP/3TP7F582YGBgaora3lD/7gD1i1atWkzXGxWOTWW2/ljTfeIJ1O8+lPf5rm5uaZ/MgzarT5vv/++/E8rzzHK1as4M/+7M8AzfdkGG3OhwsZAFx55ZV89atfLd+BmutzPieDeRERERGRI8GcS7MRERERETlSKJgXEREREalQCuZFRERERCqUgnkRERERkQqlYF5EREREpEIpmBcRmUF/+7d/yy9/+ctDOrazs5OPf/zjBEEwyaM6sN7eXr7whS/wR3/0R/zrv/7rfs/fdtttfP/73x/z+D/4gz+gra3tsMZw5ZVX8vzzzx/WOURE5gIF8yIiFWLfALaxsZF77rkH05ze/5Vv2LCB6upq7r77bv7oj/5oWt9bRERGUjAvIiIT0tnZyYIFC+Zkkybf92d6CCIiE2LP9ABERGaDK6+8krVr1/LYY4/R29vLqlWr+JM/+RNisRgQrUb/27/9G4ODgxx33HH86Z/+KQ0NDUCUNrJu3Tp+9rOfkcvlOP/88/nDP/xDTNPkhz/8IW1tbVxzzTUAtLe3c9VVV3HvvfdiWdaIMbS1tXHHHXewbds2DMPglFNO4b//9/9OVVUVt9xyC52dndx4442YpsmHPvQhzjrrrBHn6u7u5lvf+hYvv/wy6XSaiy++mLVr1wJR59WdO3cSi8X4r//6LxobG7nyyitZtmzZqPOxZcsW7rrrLnbt2sX8+fNZt24dxx57LLfddhuPP/44AD/96U/5y7/8S04++eT9ju/v7+dLX/oSW7du5eijj+aqq66iqalpv9dls1nuvPNOnn32WeLxOBdeeCEf+MAHyncbNmzYwE9/+lO6urrIZDJcffXVLF26dMQ53nrrLf7P//k/XHbZZZx99tn7vUexWOSb3/wmTz/9NHV1dZx//vn8+7//O7fffnv5u//d3/1dHn/8cXbt2sU999zDs88+y/e+9z26u7tZsmQJf/Inf8KCBQvK3/fXv/51WlpaAMrt4z/ykY/w4osvcsstt/Dud7+bn/70pyQSCT7ykY+wZs2aUedZRORwaWVeRKTk8ccf53/9r//FLbfcwu7du/nRj34EwG9/+1vuvfdePvOZz/DNb36TpqYmvva1r404dtOmTdxwww3ceOONPPXUUzz88MOHNIYPfOAD3HHHHdx88810dXVx3333AXD11VfT2NjItddeyz333MPFF1+837Ff+9rXyGQy3HHHHfzFX/wF9957Ly+88EL5+aeffprVq1dz1113cfrpp3PnnXeOOobBwUFuuOEG3vve93LnnXdy0UUXccMNNzAwMMCVV17JOeecw8UXX8w999wzaiA/PJcf/OAH+Zd/+ReWLFnC17/+9VFfd+edd5LNZrn11lv527/9Wx577DEeeeQRAJ588knuu+8+rrzySu6++26uvfZaqqurRxz/+uuv8+Uvf5nLL7981EAe4L777qOjo4NbbrmFv/7rv+ZXv/rVfq954oknuO6667jrrrvYs2cPX/va11i3bh3f/va3WblyJTfeeCOe5416/n319vYyMDDA7bffzpVXXsk3v/lNdu3aNa5jRUQmSsG8iEjJe97zHhobG0mn03zgAx/giSeeAOBXv/oV73rXu1i6dCmO43DZZZfxyiuv0N7eXj724osvJp1O09jYyPve977ysRPR0tLCySefjOM41NTUcNFFF7F58+ZxHdvZ2cnLL7/MH/7hHxKLxViyZAkXXnghjz32WPk1xx13HKeddhqmaXLuuefy5ptvjnquZ555hpaWFs4991wsy+Kcc85h/vz5PP300+P+LKeddhonnHACjuPw0Y9+lFdeeYXOzs4RrwmCgI0bN3LZZZeRTCaZN28e73//+8tjfuihh7j44otZvnw5hmHQ0tIyYnX/5Zdf5u/+7u+48soreec73znmWJ588kk+8IEPkE6nyWQyvPe9793vNe9973tpbGwkFouxceNGVq5cycknn4xt2/ze7/0exWKRLVu2jPvzX3rppTiOwwknnMDKlSvZuHHjuI8VEZkIpdmIiJQ0NjaWf25qaqK7uxuAnp4ejj766PJziUSCdDpNd3c38+bNAyCTyYw4tqenZ8Lv39fXx3e+8x1eeukl8vk8QRCQTqfHdWxPTw/pdJpkMjni87z22mvl32tra8s/x2IxXNfF9/390n26u7v3S4nZez7GY+/5GJ6vnp6eEXPc39+P53ljzntnZyfNzc1jvsd//Md/cPzxx/OOd7yj/NivfvUrvvnNbwJw/PHH8/nPf36/9917bMP2fr6np2fE5zdNk8bGxnF//qqqKhKJxIjPdCh/HkRExkMr8yIiJXuvHHd2dpZz4uvr60c8l8/nGRwcLD8P0NXVNeLY+vp6IApki8Vi+bne3t4x3/973/seAP/wD//A3XffzdVXX00YhuMae319PYODg+RyuVE/w0Q0NDTQ0dEx4rGJnmvv+Rier+E5GVZTU4NlWWPOe2NjI3v27BnzPf70T/+Urq4u7rrrrvJja9as4Z577uGee+7h85//PAB1dXUjxrP3z6Opr68f8fnDMBwxrng8TqFQKD+/73c6NDREPp8f8Zn2/ewiIpNFwbyISMmDDz5IV1cXg4OD3H///Zx11lkAnHPOOTz88MO8+eabuK7Lvffey/Lly8ur8gA//vGPGRwcpLOzk5/97GesXr0agCVLlvDSSy/R2dlJNptl/fr1Y75/LpcjkUhQVVVFd3c3DzzwwIjn6+rqRqT27K2xsZFjjz2W733vexSLRbZt28bDDz98SBsvV65cye7du3n88cfxfZ+NGzeyc+dOTjvttHGf49lnn+Xll1/G8zy+//3vs2LFihGr3xCteJ911lnce++95HI5Ojo6+MlPflIe8wUXXMADDzzA66+/ThiGtLW1jQiyE4kEn//853nppZf4v//3/445lrPOOov169czODhId3c3P//5zw849tWrV/Pss8/ywgsv4HkeDzzwAI7jcOyxxwLRd/r4448TBAHPPffcqKlQP/zhD/E8j5deeolnnnmm/GdJRGSyKc1GRKTknHPO4ctf/jI9PT2cfvrpfPCDHwTgpJNO4tJLL+Wmm25icHCQY489lk9/+tMjjj399NO57rrryGaznH/++VxwwQUAnHzyyZx11ll87nOfo7q6mosvvpinnnpq1Pf/8Ic/zK233sof//Efl3PWf/rTn5afv+SSS7jzzjv57ne/y+///u9z5plnjjj+z//8z/nWt77FFVdcQTqd5sMf/vCYG1QPpLq6muuuu47vfOc7fOtb36KlpYXrrruOmpqacZ/j7LPP5r777uOVV15h6dKl5Wo++7r88su58847ueqqq4jFYlx44YW8613vAqIgfGBggK997WvllKZ9q+JUVVXxN3/zN3zxi1/Esiw+8pGP7PceH/rQh/jWt77FVVddRX19Peecc055k+1o5s+fz9VXX82dd95ZrmZz7bXXYtvRX5nr1q3jtttu48EHH2TVqlWsWrVqxPF1dXWk02muuOIKYrEYf/qnf0pra+u4505EZCKMcLz3cEVE5rArr7ySK6644pCC331LFcrs9otf/IInnniCL37xi5N+7uHSlMNlL0VEpprSbEREZE7r6enh5ZdfJggCdu3axQMPPMDv/M7vzPSwREQmhdJsRERkTvM8j29961u0t7eTSqU4++yzec973jPTwxIRmRRKsxERERERqVBKsxERERERqVAK5kVEREREKpSCeRERERGRCqVgXkRERESkQimYFxERERGpUP8fOfz+39QDumQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x468 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12.5, 6.5 )\n",
    "data = np.genfromtxt( \"./data/census_data.csv\", skip_header=1, \n",
    "                        delimiter= \",\")\n",
    "plt.scatter( data[:,1], data[:,0], alpha = 0.5, c=\"#7A68A6\")\n",
    "plt.title(\"Census mail-back rate vs Population\")\n",
    "plt.ylabel(\"Mail-back rate\")\n",
    "plt.xlabel(\"population of block-group\")\n",
    "plt.xlim(-100, 15e3 )\n",
    "plt.ylim( -5, 105)\n",
    "\n",
    "i_min = np.argmin(  data[:,0] )\n",
    "i_max = np.argmax(  data[:,0] )\n",
    " \n",
    "plt.scatter( [ data[i_min,1], data[i_max, 1] ], \n",
    "             [ data[i_min,0],  data[i_max,0] ],\n",
    "             s = 60, marker = \"o\", facecolors = \"none\",\n",
    "             edgecolors = \"#A60628\", linewidths = 1.5, \n",
    "             label=\"most extreme points\")\n",
    "\n",
    "plt.legend(scatterpoints = 1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is a classic phenomenon in statistics. I say *classic* referring to the \"shape\" of the scatter plot above. It follows a classic triangular form, that tightens as we increase the sample size (as the Law of Large Numbers becomes more exact). \n",
    "\n",
    "I am perhaps overstressing the point and maybe I should have titled the book *\"You don't have big data problems!\"*, but here again is an example of the trouble with *small datasets*, not big ones. Simply, small datasets cannot be processed using the Law of Large Numbers. Compare with applying the Law without hassle to big datasets (ex. big data). I mentioned earlier that paradoxically big data prediction problems are solved by relatively simple algorithms. The paradox is partially resolved by understanding that the Law of Large Numbers creates solutions that are *stable*, i.e. adding or subtracting a few data points will not affect the solution much. On the other hand, adding or removing data points to a small dataset can create very different results. \n",
    "\n",
    "For further reading on the hidden dangers of the Law of Large Numbers, I would highly recommend the excellent manuscript [The Most Dangerous Equation](http://nsm.uh.edu/~dgraur/niv/TheMostDangerousEquation.pdf). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: How to order Reddit submissions\n",
    "\n",
    "You may have disagreed with the original statement that the Law of Large numbers is known to everyone, but only implicitly in our subconscious decision making. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer? 2 reviewers? 3 reviewers? We implicitly understand that with such few reviewers that the average rating is **not** a good reflection of the true value of the product.\n",
    "\n",
    "This has created flaws in how we sort items, and more generally, how we compare items. Many people have realized that sorting online search results by their rating, whether the objects be books, videos, or online comments, return poor results. Often the seemingly top videos or comments have perfect ratings only from a few enthusiastic fans, and truly more quality videos or comments are hidden in later pages with *falsely-substandard* ratings of around 4.8. How can we correct this?\n",
    "\n",
    "Consider the popular site Reddit (I purposefully did not link to the website as you would never come back). The site hosts links to stories or images, called submissions, for people to comment on. Redditors can vote up or down on each submission (called upvotes and downvotes). Reddit, by default, will sort submissions to a given subreddit by Hot, that is, the submissions that have the most upvotes recently.\n",
    "\n",
    "<img src=\"http://i.imgur.com/3v6bz9f.png\" />\n",
    "\n",
    "\n",
    "How would you determine which submissions are the best? There are a number of ways to achieve this:\n",
    "\n",
    "1. *Popularity*: A submission is considered good if it has many upvotes. A problem with this model is that a submission with hundreds of upvotes, but thousands of downvotes. While being very *popular*, the submission is likely more controversial than best.\n",
    "2. *Difference*: Using the *difference* of upvotes and downvotes. This solves the above problem, but fails when we consider the temporal nature of submission. Depending on when a submission is posted, the website may be experiencing high or low traffic. The difference method will bias the *Top* submissions to be the those made during high traffic periods, which have accumulated more upvotes than submissions that were not so graced, but are not necessarily the best.\n",
    "3. *Time adjusted*:  Consider using Difference divided by the age of the submission. This creates a *rate*, something like *difference per second*, or *per minute*. An immediate counter-example is, if we use per second, a 1 second old submission with 1 upvote would be better than a 100 second old submission with 99 upvotes. One can avoid this by only considering at least t second old submission. But what is a good t value? Does this mean no submission younger than t is good? We end up comparing unstable quantities with stable quantities (young vs. old submissions).\n",
    "3. *Ratio*: Rank submissions by the ratio of upvotes to total number of votes (upvotes plus downvotes). This solves the temporal issue, such that new submissions who score well can be considered Top just as likely as older submissions, provided they have many upvotes to total votes. The problem here is that a submission with a single upvote (ratio = 1.0) will beat a submission with 999 upvotes and 1 downvote (ratio = 0.999), but clearly the latter submission is *more likely* to be better.\n",
    "\n",
    "I used the phrase *more likely* for good reason. It is possible that the former submission, with a single upvote, is in fact a better submission than the later with 999 upvotes. The hesitation to agree with this is because we have not seen the other 999 potential votes the former submission might get. Perhaps it will achieve an additional 999 upvotes and 0 downvotes and be considered better than the latter, though not likely.\n",
    "\n",
    "What we really want is an estimate of the *true upvote ratio*. Note that the true upvote ratio is not the same as the observed upvote ratio: the true upvote ratio is hidden, and we only observe upvotes vs. downvotes (one can think of the true upvote ratio as \"what is the underlying probability someone gives this submission a upvote, versus a downvote\"). So the 999 upvote/1 downvote submission probably has a true upvote ratio close to 1, which we can assert with confidence thanks to the Law of Large Numbers, but on the other hand we are much less certain about the true upvote ratio of the submission with only a single upvote. Sounds like a Bayesian problem to me.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to determine a prior on the upvote ratio is to look at the historical distribution of upvote ratios. This can be accomplished by scraping Reddit's submissions and determining a distribution. There are a few problems with this technique though:\n",
    "\n",
    "1. Skewed data:  The vast majority of submissions have very few votes, hence there will be many submissions with ratios near the extremes (see the \"triangular plot\" in the above Kaggle dataset), effectively skewing our distribution to the extremes. One could try to only use submissions with votes greater than some threshold. Again, problems are encountered. There is a tradeoff between number of submissions available to use and a higher threshold with associated ratio precision. \n",
    "2. Biased data: Reddit is composed of different subpages, called subreddits. Two examples are *r/aww*, which posts pics of cute animals, and *r/politics*. It is very likely that the user behaviour towards submissions of these two subreddits are very different: visitors are likely friendly and affectionate in the former, and would therefore upvote submissions more, compared to the latter, where submissions are likely to be controversial and disagreed upon. Therefore not all submissions are the same. \n",
    "\n",
    "\n",
    "In light of these, I think it is better to use a `Uniform` prior.\n",
    "\n",
    "\n",
    "With our prior in place, we can find the posterior of the true upvote ratio. The Python script `top_showerthoughts_submissions.py` will scrape the best posts from the `showerthoughts` community on Reddit. This is a text-only community so the title of each post *is* the post. Below is the top post as well as some other sample posts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post contents: \n",
      "\n",
      "It’s socially acceptable to kiss with our mouth sphincters. But not kiss with our butt sphincters\n"
     ]
    }
   ],
   "source": [
    "#adding a number to the end of the %run call will get the ith top post.\n",
    "#please notice that the praw lib to get info from reddit changed its authorize functions\n",
    "#u can get some info from https://praw.readthedocs.io/en/latest/getting_started/configuration/prawini.html\n",
    "%run top_showerthoughts_submissions.py 2\n",
    "\n",
    "print(\"Post contents: \\n\")\n",
    "print(top_post)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some Submissions (out of 98 total) \n",
      "-----------\n",
      "\"Ironically, meteorologists study nothing about meteors.\"\n",
      "upvotes/downvotes:  [83 11] \n",
      "\n",
      "\"When cleaning house you are cleaning yourself off of stuff\"\n",
      "upvotes/downvotes:  [0 0] \n",
      "\n",
      "\"Every company shipping an electronic device powered by USB without an adapter just assumes you have a spare one. The irritating thing is that they’re usually not wrong.\"\n",
      "upvotes/downvotes:  [10  4] \n",
      "\n",
      "\"Cats don’t have lips and butt cheeks\"\n",
      "upvotes/downvotes:  [12  5] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "contents: an array of the text from the last 100 top submissions to a subreddit\n",
    "votes: a 2d numpy array of upvotes, downvotes for each submission.\n",
    "\"\"\"\n",
    "n_submissions = len(votes)\n",
    "submissions = np.random.randint( n_submissions, size=4)\n",
    "print(\"Some Submissions (out of %d total) \\n-----------\"%n_submissions)\n",
    "for i in submissions:\n",
    "    print('\"' + contents[i] + '\"')\n",
    "    print(\"upvotes/downvotes: \",votes[i,:], \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For a given true upvote ratio $p$ and $N$ votes, the number of upvotes will look like a Binomial random variable with parameters $p$ and $N$. (This is because of the equivalence between upvote ratio and probability of upvoting versus downvoting, out of $N$ possible votes/trials). We create a function that performs Bayesian inference on $p$, for a particular submission's upvote/downvote pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import pymc as pm\n",
    "\n",
    "def posterior_upvote_ratio( upvotes, downvotes, samples = 15000):\n",
    "    \"\"\"\n",
    "    This function accepts the number of upvotes and downvotes a particular submission recieved, \n",
    "    and the number of posterior samples to return to the user. Assumes a uniform prior.\n",
    "    \"\"\"\n",
    "    N = upvotes + downvotes\n",
    "    with pm.Model() as model:\n",
    "        upvote_ratio = pm.Uniform(\"upvote_ratio\", 0, 1)\n",
    "        observations = pm.Binomial( \"obs\",  N, upvote_ratio, observed=upvotes)\n",
    "        \n",
    "        trace = pm.sample(samples, step=pm.NUTS(),tune=5000,chains=1)\n",
    "    \n",
    "    \n",
    "    return trace.posterior.upvote_ratio.data.T\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the resulting posterior distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sequential sampling (1 chains in 1 job)\n",
      "NUTS: [upvote_ratio]\n"
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       "      100.00% [20000/20000 00:02&lt;00:00 Sampling chain 0, 0 divergences]\n",
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     "text": [
      "Sampling 1 chain for 5_000 tune and 15_000 draw iterations (5_000 + 15_000 draws total) took 3 seconds.\n",
      "The acceptance probability does not match the target. It is 0.606, but should be close to 0.8. Try to increase the number of tuning steps.\n",
      "Sequential sampling (1 chains in 1 job)\n",
      "NUTS: [upvote_ratio]\n"
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      "Sampling 1 chain for 5_000 tune and 15_000 draw iterations (5_000 + 15_000 draws total) took 3 seconds.\n",
      "Sequential sampling (1 chains in 1 job)\n",
      "NUTS: [upvote_ratio]\n"
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      "Sampling 1 chain for 5_000 tune and 15_000 draw iterations (5_000 + 15_000 draws total) took 3 seconds.\n",
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 12., 8)\n",
    "posteriors = []\n",
    "colours = [\"#348ABD\", \"#A60628\", \"#7A68A6\", \"#467821\", \"#CF4457\"]\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    posteriors.append( posterior_upvote_ratio( votes[j, 0], votes[j,1] ) )\n",
    "    plt.hist( posteriors[i], bins = 10, density = True, alpha = .9, \n",
    "            histtype=\"step\",color = colours[i%5], lw = 3,\n",
    "            label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n",
    "    plt.hist( posteriors[i], bins = 10, density = True, alpha = .2, \n",
    "            histtype=\"stepfilled\",color = colours[i], lw = 3, )\n",
    "    \n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlim( 0, 1)\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some distributions are very tight, others have very long tails (relatively speaking), expressing our uncertainty with what the true upvote ratio might be.\n",
    "\n",
    "### Sorting!\n",
    "\n",
    "We have been ignoring the goal of this exercise: how do we sort the submissions from *best to worst*? Of course, we cannot sort distributions, we must sort scalar numbers. There are many ways to distill a distribution down to a scalar: expressing the distribution through its expected value, or mean, is one way. Choosing the mean is a bad choice though. This is because the mean does not take into account the uncertainty of distributions.\n",
    "\n",
    "I  suggest using the *95% least plausible value*, defined as the value such that there is only a 5% chance the true parameter is lower (think of the lower bound on the 95% credible region). Below are the posterior distributions with the 95% least-plausible value plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0]\n",
      " [0]\n",
      " [0]\n",
      " [0]] [array([0.91212337]), array([0.78778606]), array([0.74345616]), array([0.78192709])]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize( 14., 8)\n",
    "N = posteriors[0].shape[0]\n",
    "lower_limits = []\n",
    "\n",
    "for i in range(len(submissions)):\n",
    "    j = submissions[i]\n",
    "    plt.hist( posteriors[i], bins = 20, density = True, alpha = .9, \n",
    "            histtype=\"step\",color = colours[i], lw = 3,\n",
    "            label = '(%d up:%d down)\\n%s...'%(votes[j, 0], votes[j,1], contents[j][:50]) )\n",
    "    plt.hist( posteriors[i], bins = 20, density = True, alpha = .2, \n",
    "            histtype=\"stepfilled\",color = colours[i], lw = 3, )\n",
    "    v = np.sort( posteriors[i] )[ int(0.05*N) ]\n",
    "    #plt.vlines( v, 0, 15 , color = \"k\", alpha = 1, linewidths=3 )\n",
    "    plt.vlines( v, 0, 10 , color = colours[i], linestyles = \"--\",  linewidths=3  )\n",
    "    lower_limits.append(v)\n",
    "    plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.title(\"Posterior distributions of upvote ratios on different submissions\");\n",
    "order = np.argsort( -np.array( lower_limits ) )\n",
    "print(order, lower_limits)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best submissions, according to our procedure, are the submissions that are *most-likely* to score a high percentage of upvotes. Visually those are the submissions with the 95% least plausible value close to 1.\n",
    "\n",
    "Why is sorting based on this quantity a good idea? By ordering by the 95% least plausible value, we are being the most conservative with what we think is best.  When using the lower-bound of the 95% credible interval, we believe with high certainty that the 'true upvote ratio' is at the very least equal to this value (or greater), thereby ensuring that the best submissions are still on top. Under this ordering, we impose the following very natural properties:\n",
    "\n",
    "1. given two submissions with the same observed upvote ratio, we will assign the submission with more votes as better (since we are more confident it has a higher ratio).\n",
    "2. given two submissions with the same number of votes, we still assign the submission with more upvotes as *better*.\n",
    "\n",
    "### But this is too slow for real-time!\n",
    "\n",
    "I agree, computing the posterior of every submission takes a long time, and by the time you have computed it, likely the data has changed. I delay the mathematics to the appendix, but I suggest using the following formula to compute the lower bound very fast.\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\\begin{align}\n",
    "& a = 1 + u \\\\\\\\\n",
    "& b = 1 + d \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "$u$ is the number of upvotes, and $d$ is the number of downvotes. The formula is a shortcut in Bayesian inference, which will be further explained in Chapter 6 when we discuss priors in more detail.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximate lower bounds:\n",
      "[0.60091591 0.535      0.65630332 0.02368603 0.34209586 0.39235808\n",
      " 0.3726793  0.63447009 0.28273871 0.02368603 0.93490542 0.44960117\n",
      " 0.3605993  0.50200985 0.535      0.80491887 0.69226131 0.34209586\n",
      " 0.635      0.02368603 0.02368603 0.35627885 0.7609129  0.80556899\n",
      " 0.42069919 0.45268299 0.55267475 0.91892384 0.35627885 0.64814153\n",
      " 0.02368603 0.51514069 0.39683271 0.74107856 0.02368603 0.56085485\n",
      " 0.02368603 0.40084546 0.32754218 0.02368603 0.42360758 0.83265815\n",
      " 0.93950433 0.43590471 0.02368603 0.90975941 0.02368603 0.28273871\n",
      " 0.02368603 0.02368603 0.61090389 0.02368603 0.46120533 0.59147534\n",
      " 0.02368603 0.92871189 0.27       0.94829855 0.59321297 0.02368603\n",
      " 0.4722123  0.77097389 0.82620033 0.02368603 0.82934814 0.60993921\n",
      " 0.84138267 0.49569509 0.02368603 0.77681961 0.58991998 0.32754218\n",
      " 0.02368603 0.68704735 0.65475736 0.43126186 0.27775794 0.4722123\n",
      " 0.55184141 0.42657558 0.45093892 0.73835967 0.59123643 0.02368603\n",
      " 0.47086045 0.02368603 0.02368603 0.02368603 0.7249286  0.02368603\n",
      " 0.02368603 0.89819827 0.02368603 0.02368603 0.94269956 0.30828372\n",
      " 0.83540757 0.02368603]\n",
      "\n",
      "\n",
      "Top 40 Sorted according to approximate lower bounds:\n",
      "\n",
      "\n",
      "951 40 Finland is 1 country away from North Korea.\n",
      "-------------\n",
      "2493 131 Alcohol is a good solution for organic compound, but not a good solution for problems in life.\n",
      "-------------\n",
      "297 12 Odds are that many brilliant ideas and inventions went undeveloped, because people were too lazy or pessimistic to follow through\n",
      "-------------\n",
      "106 3 Finding your groceries in a new or different grocery store is an adulting scavenger hunt.\n",
      "-------------\n",
      "167 7 Chocolate chip pancakes is a loophole to have cookies for breakfast.\n",
      "-------------\n",
      "1476 111 Whoever comes up with the recommended serving sizes on food packing must not have much of an appetite.\n",
      "-------------\n",
      "456 34 Due to the declining population of people who own houses with garages and have kids, drummers are probably going to become very rare in the next couple generations\n",
      "-------------\n",
      "1625 161 Life is not a gift because you cannot reject it.\n",
      "-------------\n",
      "907 148 You can kill a fire but you can’t kill a water\n",
      "-------------\n",
      "86 10 Majority of us can't remember a single card that came with a gift but has spent countless hours looking for cards when giving gifts\n",
      "-------------\n",
      "30 2 Humans are too unreliable to maintain most hypothetical conspiracies\n",
      "-------------\n",
      "57 6 Omniscience seems great until you think about all the stuff you wouldn’t want to know\n",
      "-------------\n",
      "288 47 The average six-month-old dog probably knows more English than the average year-old human\n",
      "-------------\n",
      "11 0 Nothing sounds more lonely than going through an Escape Room by yourself.\n",
      "-------------\n",
      "43 5 The Piston Cup trophy is an ornamented internal organ.\n",
      "-------------\n",
      "136 28 It is possible that, at some point in history, a bullet randomly hit an ant.\n",
      "-------------\n",
      "40 6 Being read bedtime stories as a kid is probably why a lot of people watch TV to fall asleep.\n",
      "-------------\n",
      "29 4 Radiation can both cause and eliminate cancer.\n",
      "-------------\n",
      "22 3 The Roman Empire did it all without coffee.\n",
      "-------------\n",
      "111 28 It's amazing that in just over 100 years that humans have collectively developed the instinct to hit the brakes when they see a cop.\n",
      "-------------\n",
      "28 5 Every day, the chance of a bridge collapsing increases\n",
      "-------------\n",
      "14 2 When you cut corners, it just creates even more corners to deal with.\n",
      "-------------\n",
      "10 1 An explanation can never equal an experience and yet most of our recent human development has focused on increasing our ability to explain.\n",
      "-------------\n",
      "12 2 No one ever won an argument with their spouse by winning.\n",
      "-------------\n",
      "36 11 Slow motion makes any scene look dramatic while fast motion makes it look comical\n",
      "-------------\n",
      "20 5 A large portion of the world probably doesn’t know the pure rage that medical commercials induce.\n",
      "-------------\n",
      "11 2 A surprise birthday party is not really a surprise because we both knew it was your birthday.\n",
      "-------------\n",
      "8 1 Watching too many freakout videos on social media is why people believe the whole world is crazy.\n",
      "-------------\n",
      "10 2 It's impolite to post a picture of a stranger's license plate. Except when the licence plate is a word.\n",
      "-------------\n",
      "28 10 Fingerless gloves can’t be worn without fingers\n",
      "-------------\n",
      "4 0 Movies depicting rushed cross country road trips always show the characters driving down one lane country roads in the middle of nowhere instead of the highway for no fucking reason.\n",
      "-------------\n",
      "32 13 The universe experiences you just as much as you experience the universe\n",
      "-------------\n",
      "28 11 People don't downsize because the kids moved out, they downsized so the kids don't move back in.\n",
      "-------------\n",
      "16 5 Landlines that use the internet instead of a phone line are reverse dial-up\n",
      "-------------\n",
      "18 6 Amish people live like it's the early 18th century so in the 25th century there might be people who live like it's the 21st century\n",
      "-------------\n",
      "6 1 Winning a low chance carnival game as a kid really built up gambling addiction as an adult.\n",
      "-------------\n",
      "12 4 The main difference between humans and robots is that it's harder to replace human parts when they start to fail than it is to replace robot parts when they fail.\n",
      "-------------\n",
      "8 2 A hummingbird is a vampire of the flower world\n",
      "-------------\n",
      "13 5 Before the camera was invented, no one had seen themselves with their eyes closed.\n",
      "-------------\n",
      "13 5 Some birds probably have a fear of heights\n",
      "-------------\n"
     ]
    }
   ],
   "source": [
    "def intervals(u,d):\n",
    "    a = 1. + u\n",
    "    b = 1. + d\n",
    "    mu = a/(a+b)\n",
    "    std_err = 1.65*np.sqrt( (a*b)/( (a+b)**2*(a+b+1.) ) )\n",
    "    return ( mu, std_err )\n",
    "\n",
    "print(\"Approximate lower bounds:\")\n",
    "posterior_mean, std_err  = intervals(votes[:,0],votes[:,1])\n",
    "lb = posterior_mean - std_err\n",
    "print(lb)\n",
    "print(\"\\n\")\n",
    "print(\"Top 40 Sorted according to approximate lower bounds:\")\n",
    "print(\"\\n\")\n",
    "order = np.argsort( -lb )\n",
    "ordered_contents = []\n",
    "for i in order[:40]:\n",
    "    ordered_contents.append( contents[i] )\n",
    "    print(votes[i,0], votes[i,1], contents[i])\n",
    "    print(\"-------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the ordering visually by plotting the posterior mean and bounds, and sorting by the lower bound. In the plot below, notice that the left error-bar is sorted (as we suggested this is the best way to determine an ordering), so the means, indicated by dots, do not follow any strong pattern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_order = order[::-1][-40:]\n",
    "plt.errorbar( posterior_mean[r_order], np.arange( len(r_order) ), \n",
    "               xerr=std_err[r_order], capsize=0, fmt=\"o\",\n",
    "                color = \"#7A68A6\")\n",
    "plt.xlim( 0.3, 1)\n",
    "plt.yticks( np.arange( len(r_order)-1,-1,-1 ), map( lambda x: x[:30].replace(\"\\n\",\"\"), ordered_contents) );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graphic above, you can see why sorting by mean would be sub-optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extension to Starred rating systems\n",
    "\n",
    "The above procedure works well for upvote-downvotes schemes, but what about systems that use star ratings, e.g. 5 star rating systems. Similar problems apply with simply taking the average: an item with two perfect ratings would beat an item with thousands of perfect ratings, but a single sub-perfect rating. \n",
    "\n",
    "\n",
    "We can consider the upvote-downvote problem above as binary: 0 is a downvote, 1 if an upvote. A $N$-star rating system can be seen as a more continuous version of above, and we can set $n$ stars rewarded is equivalent to rewarding $\\frac{n}{N}$. For example, in a 5-star system, a 2 star rating corresponds to 0.4. A perfect rating is a 1. We can use the same formula as before, but with $a,b$ defined differently:\n",
    "\n",
    "\n",
    "$$ \\frac{a}{a + b} - 1.65\\sqrt{ \\frac{ab}{ (a+b)^2(a + b +1 ) } }$$\n",
    "\n",
    "where \n",
    "\n",
    "\\begin{align}\n",
    "& a = 1 + S \\\\\\\\\n",
    "& b = 1 + N - S \\\\\\\\\n",
    "\\end{align}\n",
    "\n",
    "where $N$ is the number of users who rated, and $S$ is the sum of all the ratings, under the equivalence scheme mentioned above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example: Counting Github stars\n",
    "\n",
    "What is the average number of stars a Github repository has? How would you calculate this? There are over 6 million respositories, so there is more than enough data to invoke the Law of Large numbers. Let's start pulling some data. TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "While the Law of Large Numbers is cool, it is only true so much as its name implies: with large sample sizes only. We have seen how our inference can be affected by not considering *how the data is shaped*. \n",
    "\n",
    "1. By (cheaply) drawing many samples from the posterior distributions, we can ensure that the Law of Large Number applies as we approximate expected values (which we will do in the next chapter).\n",
    "\n",
    "2. Bayesian inference understands that with small sample sizes, we can observe wild randomness. Our posterior distribution will reflect this by being more spread rather than tightly concentrated. Thus, our inference should be correctable.\n",
    "\n",
    "3. There are major implications of not considering the sample size, and trying to sort objects that are unstable leads to pathological orderings. The method provided above solves this problem.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix\n",
    "\n",
    "##### Derivation of sorting submissions formula\n",
    "\n",
    "Basically what we are doing is using a Beta prior (with parameters $a=1, b=1$, which is a uniform distribution), and using a Binomial likelihood with observations $u, N = u+d$. This means our posterior is a Beta distribution with parameters $a' = 1 + u, b' = 1 + (N - u) = 1+d$. We then need to find the value, $x$, such that 0.05 probability is less than $x$. This is usually done by inverting the CDF ([Cumulative Distribution Function](http://en.wikipedia.org/wiki/Cumulative_Distribution_Function)), but the CDF of the beta, for integer parameters, is known but is a large sum [3]. \n",
    "\n",
    "We instead use a Normal approximation. The mean of the Beta is $\\mu = a'/(a'+b')$ and the variance is \n",
    "\n",
    "$$\\sigma^2 = \\frac{a'b'}{ (a' + b')^2(a'+b'+1) }$$\n",
    "\n",
    "Hence we solve the following equation for $x$ and have an approximate lower bound. \n",
    "\n",
    "$$ 0.05 = \\Phi\\left( \\frac{(x - \\mu)}{\\sigma}\\right) $$ \n",
    "\n",
    "$\\Phi$ being the [cumulative distribution for the normal distribution](http://en.wikipedia.org/wiki/Normal_distribution#Cumulative_distribution)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercises\n",
    "\n",
    "1\\. How would you estimate the quantity $E\\left[ \\cos{X} \\right]$, where $X \\sim \\text{Exp}(4)$? What about $E\\left[ \\cos{X} | X \\lt 1\\right]$, i.e. the expected value *given* we know $X$ is less than 1? Would you need more samples than the original samples size to be equally accurate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "## Enter code here\n",
    "import scipy.stats as stats\n",
    "exp = stats.expon( scale=4 )\n",
    "N = 1e5\n",
    "X = exp.rvs( int(N) )\n",
    "## ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2\\. The following table was located in the paper \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression\" [2]. The table ranks football field-goal kickers by their percent of non-misses. What mistake have the researchers made?\n",
    "\n",
    "-----\n",
    "\n",
    "####  Kicker Careers Ranked by Make Percentage\n",
    "<table><tbody><tr><th>Rank </th><th>Kicker </th><th>Make % </th><th>Number  of Kicks</th></tr><tr><td>1 </td><td>Garrett Hartley </td><td>87.7 </td><td>57</td></tr><tr><td>2</td><td> Matt Stover </td><td>86.8 </td><td>335</td></tr><tr><td>3 </td><td>Robbie Gould </td><td>86.2 </td><td>224</td></tr><tr><td>4 </td><td>Rob Bironas </td><td>86.1 </td><td>223</td></tr><tr><td>5</td><td> Shayne Graham </td><td>85.4 </td><td>254</td></tr><tr><td>… </td><td>… </td><td>…</td><td> </td></tr><tr><td>51</td><td> Dave Rayner </td><td>72.2 </td><td>90</td></tr><tr><td>52</td><td> Nick Novak </td><td>71.9 </td><td>64</td></tr><tr><td>53 </td><td>Tim Seder </td><td>71.0 </td><td>62</td></tr><tr><td>54 </td><td>Jose Cortez </td><td>70.7</td><td> 75</td></tr><tr><td>55 </td><td>Wade Richey </td><td>66.1</td><td> 56</td></tr></tbody></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In August 2013, [a popular post](http://bpodgursky.wordpress.com/2013/08/21/average-income-per-programming-language/) on the average income per programmer of different languages was trending. Here's the summary chart: (reproduced without permission, cause when you lie with stats, you gunna get the hammer). What do you notice about the extremes?\n",
    "\n",
    "------\n",
    "\n",
    "#### Average household income by programming language\n",
    "\n",
    "<table >\n",
    " <tr><td>Language</td><td>Average Household Income ($)</td><td>Data Points</td></tr>\n",
    " <tr><td>Puppet</td><td>87,589.29</td><td>112</td></tr>\n",
    " <tr><td>Haskell</td><td>89,973.82</td><td>191</td></tr>\n",
    " <tr><td>PHP</td><td>94,031.19</td><td>978</td></tr>\n",
    " <tr><td>CoffeeScript</td><td>94,890.80</td><td>435</td></tr>\n",
    " <tr><td>VimL</td><td>94,967.11</td><td>532</td></tr>\n",
    " <tr><td>Shell</td><td>96,930.54</td><td>979</td></tr>\n",
    " <tr><td>Lua</td><td>96,930.69</td><td>101</td></tr>\n",
    " <tr><td>Erlang</td><td>97,306.55</td><td>168</td></tr>\n",
    " <tr><td>Clojure</td><td>97,500.00</td><td>269</td></tr>\n",
    " <tr><td>Python</td><td>97,578.87</td><td>2314</td></tr>\n",
    " <tr><td>JavaScript</td><td>97,598.75</td><td>3443</td></tr>\n",
    " <tr><td>Emacs Lisp</td><td>97,774.65</td><td>355</td></tr>\n",
    " <tr><td>C#</td><td>97,823.31</td><td>665</td></tr>\n",
    " <tr><td>Ruby</td><td>98,238.74</td><td>3242</td></tr>\n",
    " <tr><td>C++</td><td>99,147.93</td><td>845</td></tr>\n",
    " <tr><td>CSS</td><td>99,881.40</td><td>527</td></tr>\n",
    " <tr><td>Perl</td><td>100,295.45</td><td>990</td></tr>\n",
    " <tr><td>C</td><td>100,766.51</td><td>2120</td></tr>\n",
    " <tr><td>Go</td><td>101,158.01</td><td>231</td></tr>\n",
    " <tr><td>Scala</td><td>101,460.91</td><td>243</td></tr>\n",
    " <tr><td>ColdFusion</td><td>101,536.70</td><td>109</td></tr>\n",
    " <tr><td>Objective-C</td><td>101,801.60</td><td>562</td></tr>\n",
    " <tr><td>Groovy</td><td>102,650.86</td><td>116</td></tr>\n",
    " <tr><td>Java</td><td>103,179.39</td><td>1402</td></tr>\n",
    " <tr><td>XSLT</td><td>106,199.19</td><td>123</td></tr>\n",
    " <tr><td>ActionScript</td><td>108,119.47</td><td>113</td></tr>\n",
    "</table>"
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    "### References\n",
    "\n",
    "1. Wainer, Howard. *The Most Dangerous Equation*. American Scientist, Volume 95.\n",
    "2. Clarck, Torin K., Aaron W. Johnson, and Alexander J. Stimpson. \"Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression.\" (2013): n. page. [Web](http://www.sloansportsconference.com/wp-content/uploads/2013/Going%20for%20Three%20Predicting%20the%20Likelihood%20of%20Field%20Goal%20Success%20with%20Logistic%20Regression.pdf). 20 Feb. 2013.\n",
    "3. http://en.wikipedia.org/wiki/Beta_function#Incomplete_beta_function"
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